Overview

Dataset statistics

Number of variables42
Number of observations23748
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.6 MiB
Average record size in memory336.0 B

Variable types

Numeric8
Categorical34

Alerts

title has a high cardinality: 23681 distinct valuesHigh cardinality
genres has a high cardinality: 992 distinct valuesHigh cardinality
synopsis has a high cardinality: 19634 distinct valuesHigh cardinality
producers has a high cardinality: 4351 distinct valuesHigh cardinality
licensors has a high cardinality: 265 distinct valuesHigh cardinality
studios has a high cardinality: 1518 distinct valuesHigh cardinality
duration has a high cardinality: 330 distinct valuesHigh cardinality
image_url has a high cardinality: 23590 distinct valuesHigh cardinality
id is highly overall correlated with popularity and 2 other fieldsHigh correlation
score is highly overall correlated with rank and 4 other fieldsHigh correlation
rank is highly overall correlated with score and 4 other fieldsHigh correlation
popularity is highly overall correlated with id and 5 other fieldsHigh correlation
favorites is highly overall correlated with score and 4 other fieldsHigh correlation
scored_by is highly overall correlated with id and 5 other fieldsHigh correlation
members is highly overall correlated with id and 5 other fieldsHigh correlation
type is highly overall correlated with genre__ and 1 other fieldsHigh correlation
source is highly overall correlated with genre_hentaiHigh correlation
rating is highly overall correlated with genre_hentaiHigh correlation
genre__ is highly overall correlated with typeHigh correlation
genre_hentai is highly overall correlated with type and 2 other fieldsHigh correlation
status is highly imbalanced (89.1%)Imbalance
producers is highly imbalanced (51.4%)Imbalance
licensors is highly imbalanced (78.5%)Imbalance
genre_award_winning is highly imbalanced (91.8%)Imbalance
genre_slice_of_life is highly imbalanced (62.3%)Imbalance
genre_erotica is highly imbalanced (97.7%)Imbalance
genre_romance is highly imbalanced (58.6%)Imbalance
genre_horror is highly imbalanced (84.7%)Imbalance
genre_boys_love is highly imbalanced (94.1%)Imbalance
genre_girls_love is highly imbalanced (95.9%)Imbalance
genre_sports is highly imbalanced (79.9%)Imbalance
genre_gourmet is highly imbalanced (94.8%)Imbalance
genre_suspense is highly imbalanced (92.2%)Imbalance
genre_supernatural is highly imbalanced (67.1%)Imbalance
genre_avant_garde is highly imbalanced (78.8%)Imbalance
genre_hentai is highly imbalanced (66.4%)Imbalance
genre_mystery is highly imbalanced (78.2%)Imbalance
genre_ecchi is highly imbalanced (79.1%)Imbalance
episodes is highly skewed (γ1 = 26.35007497)Skewed
favorites is highly skewed (γ1 = 26.37837039)Skewed
title is uniformly distributedUniform
id has unique valuesUnique
favorites has 10048 (42.3%) zerosZeros

Reproduction

Analysis started2024-01-02 00:04:28.299980
Analysis finished2024-01-02 00:04:50.133868
Duration21.83 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23748
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28891.562
Minimum1
Maximum55735
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size185.7 KiB
2024-01-01T21:04:50.322874image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1300.35
Q19881.75
median33540.5
Q344146.25
95-th percentile53602.3
Maximum55735
Range55734
Interquartile range (IQR)34264.5

Descriptive statistics

Standard deviation17900.231
Coefficient of variation (CV)0.61956606
Kurtosis-1.3512594
Mean28891.562
Median Absolute Deviation (MAD)14388
Skewness-0.26938822
Sum6.8611681 × 108
Variance3.2041828 × 108
MonotonicityNot monotonic
2024-01-01T21:04:50.529899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
40282 1
 
< 0.1%
40302 1
 
< 0.1%
40301 1
 
< 0.1%
40299 1
 
< 0.1%
40298 1
 
< 0.1%
40297 1
 
< 0.1%
40295 1
 
< 0.1%
40288 1
 
< 0.1%
40286 1
 
< 0.1%
Other values (23738) 23738
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
15 1
< 0.1%
16 1
< 0.1%
17 1
< 0.1%
18 1
< 0.1%
19 1
< 0.1%
ValueCountFrequency (%)
55735 1
< 0.1%
55734 1
< 0.1%
55733 1
< 0.1%
55730 1
< 0.1%
55729 1
< 0.1%
55728 1
< 0.1%
55726 1
< 0.1%
55725 1
< 0.1%
55724 1
< 0.1%
55723 1
< 0.1%

title
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct23681
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size185.7 KiB
gintama
 
4
dog days
 
3
duel masters
 
3
working
 
3
youkai watch
 
3
Other values (23676)
23732 

Length

Max length126
Median length96
Mean length24.160098
Min length1

Characters and Unicode

Total characters573754
Distinct characters39
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23622 ?
Unique (%)99.5%

Sample

1st rowcowboy bebop
2nd rowcowboy bebop tengoku no tobira
3rd rowtrigun
4th rowwitch hunter robin
5th rowbouken ou beet

Common Values

ValueCountFrequency (%)
gintama 4
 
< 0.1%
dog days 3
 
< 0.1%
duel masters 3
 
< 0.1%
working 3
 
< 0.1%
youkai watch 3
 
< 0.1%
himawari 3
 
< 0.1%
shinryaku ika musume 3
 
< 0.1%
5-toubun no hanayome 2
 
< 0.1%
kimi to boku 2
 
< 0.1%
morita-san wa mukuchi 2
 
< 0.1%
Other values (23671) 23720
99.9%

Length

2024-01-01T21:04:50.876891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
no 5552
 
5.7%
to 1043
 
1.1%
the 982
 
1.0%
movie 824
 
0.8%
ni 679
 
0.7%
season 657
 
0.7%
650
 
0.7%
wa 648
 
0.7%
de 559
 
0.6%
wo 539
 
0.6%
Other values (20362) 85423
87.6%

Most occurring characters

ValueCountFrequency (%)
73808
12.9%
a 56305
 
9.8%
i 48808
 
8.5%
o 46772
 
8.2%
n 43619
 
7.6%
e 36954
 
6.4%
u 33422
 
5.8%
s 28389
 
4.9%
t 22934
 
4.0%
h 21449
 
3.7%
Other values (29) 161294
28.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 491059
85.6%
Space Separator 73808
 
12.9%
Decimal Number 4933
 
0.9%
Dash Punctuation 3537
 
0.6%
Other Punctuation 417
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 56305
11.5%
i 48808
 
9.9%
o 46772
 
9.5%
n 43619
 
8.9%
e 36954
 
7.5%
u 33422
 
6.8%
s 28389
 
5.8%
t 22934
 
4.7%
h 21449
 
4.4%
k 20763
 
4.2%
Other values (16) 131644
26.8%
Decimal Number
ValueCountFrequency (%)
2 1470
29.8%
0 854
17.3%
1 708
14.4%
3 533
 
10.8%
9 332
 
6.7%
5 279
 
5.7%
4 277
 
5.6%
7 172
 
3.5%
8 169
 
3.4%
6 139
 
2.8%
Space Separator
ValueCountFrequency (%)
73808
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3537
100.0%
Other Punctuation
ValueCountFrequency (%)
, 417
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 491059
85.6%
Common 82695
 
14.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 56305
11.5%
i 48808
 
9.9%
o 46772
 
9.5%
n 43619
 
8.9%
e 36954
 
7.5%
u 33422
 
6.8%
s 28389
 
5.8%
t 22934
 
4.7%
h 21449
 
4.4%
k 20763
 
4.2%
Other values (16) 131644
26.8%
Common
ValueCountFrequency (%)
73808
89.3%
- 3537
 
4.3%
2 1470
 
1.8%
0 854
 
1.0%
1 708
 
0.9%
3 533
 
0.6%
, 417
 
0.5%
9 332
 
0.4%
5 279
 
0.3%
4 277
 
0.3%
Other values (3) 480
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 573754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
73808
12.9%
a 56305
 
9.8%
i 48808
 
8.5%
o 46772
 
8.2%
n 43619
 
7.6%
e 36954
 
6.4%
u 33422
 
5.8%
s 28389
 
4.9%
t 22934
 
4.0%
h 21449
 
3.7%
Other values (29) 161294
28.1%

score
Real number (ℝ)

Distinct567
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8748509
Minimum-1
Maximum9.1
Zeros0
Zeros (%)0.0%
Negative8064
Negative (%)34.0%
Memory size185.7 KiB
2024-01-01T21:04:51.107865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median5.71
Q36.68
95-th percentile7.69
Maximum9.1
Range10.1
Interquartile range (IQR)7.68

Descriptive statistics

Standard deviation3.5761126
Coefficient of variation (CV)0.92290328
Kurtosis-1.5315613
Mean3.8748509
Median Absolute Deviation (MAD)1.41
Skewness-0.54057151
Sum92019.96
Variance12.788582
MonotonicityNot monotonic
2024-01-01T21:04:51.334863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 8064
34.0%
6.31 80
 
0.3%
6.54 80
 
0.3%
6.25 79
 
0.3%
6.51 79
 
0.3%
6.52 78
 
0.3%
6.5 76
 
0.3%
6.36 75
 
0.3%
5.99 74
 
0.3%
6.32 74
 
0.3%
Other values (557) 14989
63.1%
ValueCountFrequency (%)
-1 8064
34.0%
1.85 1
 
< 0.1%
1.98 1
 
< 0.1%
2.22 2
 
< 0.1%
2.3 1
 
< 0.1%
2.37 1
 
< 0.1%
2.53 1
 
< 0.1%
2.59 1
 
< 0.1%
2.63 1
 
< 0.1%
2.74 1
 
< 0.1%
ValueCountFrequency (%)
9.1 1
 
< 0.1%
9.07 2
< 0.1%
9.06 1
 
< 0.1%
9.05 3
< 0.1%
9.04 3
< 0.1%
9.03 1
 
< 0.1%
9.02 1
 
< 0.1%
9 1
 
< 0.1%
8.98 2
< 0.1%
8.94 2
< 0.1%

genres
Categorical

Distinct992
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size185.7 KiB
['-']
4505 
['comedy']
2225 
['fantasy']
 
1182
['hentai']
 
1172
['avant garde']
 
610
Other values (987)
14054 

Length

Max length93
Median length85
Mean length18.409298
Min length5

Characters and Unicode

Total characters437184
Distinct characters27
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique422 ?
Unique (%)1.8%

Sample

1st row['award winning', 'action', 'sci-fi']
2nd row['action', 'sci-fi']
3rd row['adventure', 'action', 'sci-fi']
4th row['mystery', 'supernatural', 'action', 'drama']
5th row['adventure', 'supernatural', 'fantasy']

Common Values

ValueCountFrequency (%)
['-'] 4505
 
19.0%
['comedy'] 2225
 
9.4%
['fantasy'] 1182
 
5.0%
['hentai'] 1172
 
4.9%
['avant garde'] 610
 
2.6%
['slice of life'] 609
 
2.6%
['drama'] 592
 
2.5%
['adventure', 'fantasy'] 518
 
2.2%
['comedy', 'slice of life'] 504
 
2.1%
['action', 'sci-fi'] 490
 
2.1%
Other values (982) 11341
47.8%

Length

2024-01-01T21:04:51.574862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
comedy 6956
15.0%
fantasy 4955
10.7%
action 4537
9.8%
4505
9.7%
adventure 3704
 
8.0%
sci-fi 3012
 
6.5%
drama 2742
 
5.9%
romance 1982
 
4.3%
life 1732
 
3.7%
of 1732
 
3.7%
Other values (17) 10553
22.7%

Most occurring characters

ValueCountFrequency (%)
' 83280
19.0%
a 32887
 
7.5%
e 26045
 
6.0%
[ 23748
 
5.4%
] 23748
 
5.4%
22662
 
5.2%
c 19838
 
4.5%
n 19836
 
4.5%
t 18666
 
4.3%
, 17892
 
4.1%
Other values (17) 148582
34.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 258337
59.1%
Other Punctuation 101172
 
23.1%
Open Punctuation 23748
 
5.4%
Close Punctuation 23748
 
5.4%
Space Separator 22662
 
5.2%
Dash Punctuation 7517
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 32887
12.7%
e 26045
10.1%
c 19838
 
7.7%
n 19836
 
7.7%
t 18666
 
7.2%
o 17622
 
6.8%
i 16924
 
6.6%
r 15778
 
6.1%
d 14441
 
5.6%
s 14399
 
5.6%
Other values (11) 61901
24.0%
Other Punctuation
ValueCountFrequency (%)
' 83280
82.3%
, 17892
 
17.7%
Open Punctuation
ValueCountFrequency (%)
[ 23748
100.0%
Close Punctuation
ValueCountFrequency (%)
] 23748
100.0%
Space Separator
ValueCountFrequency (%)
22662
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7517
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 258337
59.1%
Common 178847
40.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 32887
12.7%
e 26045
10.1%
c 19838
 
7.7%
n 19836
 
7.7%
t 18666
 
7.2%
o 17622
 
6.8%
i 16924
 
6.6%
r 15778
 
6.1%
d 14441
 
5.6%
s 14399
 
5.6%
Other values (11) 61901
24.0%
Common
ValueCountFrequency (%)
' 83280
46.6%
[ 23748
 
13.3%
] 23748
 
13.3%
22662
 
12.7%
, 17892
 
10.0%
- 7517
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 437184
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 83280
19.0%
a 32887
 
7.5%
e 26045
 
6.0%
[ 23748
 
5.4%
] 23748
 
5.4%
22662
 
5.2%
c 19838
 
4.5%
n 19836
 
4.5%
t 18666
 
4.3%
, 17892
 
4.1%
Other values (17) 148582
34.0%

synopsis
Categorical

Distinct19634
Distinct (%)82.7%
Missing0
Missing (%)0.0%
Memory size185.7 KiB
-
3856 
furukawa taku film.
 
12
film by takashi ito.
 
12
a short animation by taku furukawa.
 
10
a short film by okamoto tadanari.
 
7
Other values (19629)
19851 

Length

Max length3750
Median length1328
Mean length324.41696
Min length1

Characters and Unicode

Total characters7704254
Distinct characters534
Distinct categories22 ?
Distinct scripts9 ?
Distinct blocks18 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19473 ?
Unique (%)82.0%

Sample

1st rowcrime is timeless. by the year 2071, humanity has expanded across the galaxy, filling the surface of other planets with settlements like those on earth. these new societies are plagued by murder, drug use, and theft, and intergalactic outlaws are hunted by a growing number of tough bounty hunters. spike spiegel and jet black pursue criminals throughout space to make a humble living. beneath his goofy and aloof demeanor, spike is haunted by the weight of his violent past. meanwhile, jet manages his own troubled memories while taking care of spike and the bebop, their ship. the duo is joined by the beautiful con artist faye valentine, odd child edward wong hau pepelu tivrusky iv, and ein, a bioengineered welsh corgi. while developing bonds and working to catch a colorful cast of criminals, the bebop crew's lives are disrupted by a menace from spike's past. as a rival's maniacal plot continues to unravel, spike must choose between life with his newfound family or revenge for his old wounds.
2nd rowanother day, another bounty—such is the life of the often unlucky crew of the bebop. however, this routine is interrupted when faye, who is chasing a fairly worthless target on mars, witnesses an oil tanker suddenly explode, causing mass hysteria. as casualties mount due to a strange disease spreading through the smoke from the blast, a whopping three hundred million woolong price is placed on the head of the supposed perpetrator. with lives at stake and a solution to their money problems in sight, the bebop crew springs into action. spike, jet, faye, and edward, followed closely by ein, split up to pursue different leads across alba city. through their individual investigations, they discover a cover-up scheme involving a pharmaceutical company, revealing a plot that reaches much further than the ragtag team of bounty hunters could have realized.
3rd rowvash the stampede is the man with a $$60,000,000,000 bounty on his head. the reason: he's a merciless villain who lays waste to all those that oppose him and flattens entire cities for fun, garnering him the title "the humanoid typhoon." he leaves a trail of death and destruction wherever he goes, and anyone can count themselves dead if they so much as make eye contact—or so the rumors say. in actuality, vash is a huge softie who claims to have never taken a life and avoids violence at all costs. with his crazy doughnut obsession and buffoonish attitude in tow, vash traverses the wasteland of the planet gunsmoke, all the while followed by two insurance agents, meryl stryfe and milly thompson, who attempt to minimize his impact on the public. but soon, their misadventures evolve into life-or-death situations as a group of legendary assassins are summoned to bring about suffering to the trio. vash's agonizing past will be unraveled and his morality and principles pushed to the breaking point.
4th rowrobin sena is a powerful craft user drafted into the stnj—a group of specialized hunters that fight deadly beings known as witches. though her fire power is great, she's got a lot to learn about her powers and working with her cool and aloof partner, amon. but the truth about the witches and herself will leave robin on an entirely new path that she never expected! (source: funimation)
5th rowit is the dark century and the people are suffering under the rule of the devil, vandel, who is able to manipulate monsters. the vandel busters are a group of people who hunt these devils, and among them, the zenon squad is known to be the strongest busters on the continent. a young boy, beet, dreams of joining the zenon squad. however, one day, as a result of beet's fault, the zenon squad was defeated by the devil, beltose. the five dying busters sacrificed their life power into their five weapons, saiga. after giving their weapons to beet, they passed away. years have passed since then and the young vandel buster, beet, begins his adventure to carry out the zenon squad's will to put an end to the dark century.

Common Values

ValueCountFrequency (%)
- 3856
 
16.2%
furukawa taku film. 12
 
0.1%
film by takashi ito. 12
 
0.1%
a short animation by taku furukawa. 10
 
< 0.1%
a short film by okamoto tadanari. 7
 
< 0.1%
short film by hirano ryou. 6
 
< 0.1%
a short puppet animation movie by tadahito mochinaga. 6
 
< 0.1%
short animation by rapparu. 6
 
< 0.1%
short film by kurosaka keita. 6
 
< 0.1%
unaired specials included in the blu-ray and dvd release. 5
 
< 0.1%
Other values (19624) 19822
83.5%

Length

2024-01-01T21:04:51.834862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 83954
 
6.4%
a 39190
 
3.0%
to 38645
 
3.0%
of 35973
 
2.8%
and 35226
 
2.7%
in 20339
 
1.6%
is 18023
 
1.4%
his 13673
 
1.1%
with 12631
 
1.0%
her 11203
 
0.9%
Other values (64461) 992853
76.3%

Most occurring characters

ValueCountFrequency (%)
1265996
16.4%
e 732720
 
9.5%
a 538240
 
7.0%
t 522334
 
6.8%
i 467905
 
6.1%
o 466981
 
6.1%
n 433271
 
5.6%
s 429811
 
5.6%
r 381011
 
4.9%
h 342660
 
4.4%
Other values (524) 2123325
27.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6162919
80.0%
Space Separator 1266003
 
16.4%
Other Punctuation 185035
 
2.4%
Control 29914
 
0.4%
Dash Punctuation 20610
 
0.3%
Decimal Number 18735
 
0.2%
Close Punctuation 9336
 
0.1%
Open Punctuation 9328
 
0.1%
Other Letter 815
 
< 0.1%
Final Punctuation 800
 
< 0.1%
Other values (12) 759
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
32
 
3.9%
19
 
2.3%
14
 
1.7%
13
 
1.6%
12
 
1.5%
12
 
1.5%
12
 
1.5%
10
 
1.2%
10
 
1.2%
8
 
1.0%
Other values (342) 673
82.6%
Lowercase Letter
ValueCountFrequency (%)
e 732720
11.9%
a 538240
 
8.7%
t 522334
 
8.5%
i 467905
 
7.6%
o 466981
 
7.6%
n 433271
 
7.0%
s 429811
 
7.0%
r 381011
 
6.2%
h 342660
 
5.6%
l 235183
 
3.8%
Other values (64) 1612803
26.2%
Other Punctuation
ValueCountFrequency (%)
, 71567
38.7%
. 68725
37.1%
' 15858
 
8.6%
" 11626
 
6.3%
: 9341
 
5.0%
! 3309
 
1.8%
? 1749
 
0.9%
; 1458
 
0.8%
/ 698
 
0.4%
& 273
 
0.1%
Other values (16) 431
 
0.2%
Other Symbol
ValueCountFrequency (%)
100
52.1%
° 25
 
13.0%
24
 
12.5%
17
 
8.9%
6
 
3.1%
5
 
2.6%
5
 
2.6%
4
 
2.1%
1
 
0.5%
1
 
0.5%
Other values (4) 4
 
2.1%
Math Symbol
ValueCountFrequency (%)
~ 81
42.9%
+ 61
32.3%
× 19
 
10.1%
= 11
 
5.8%
4
 
2.1%
3
 
1.6%
3
 
1.6%
÷ 2
 
1.1%
2
 
1.1%
1
 
0.5%
Other values (2) 2
 
1.1%
Decimal Number
ValueCountFrequency (%)
1 4110
21.9%
0 3515
18.8%
2 3249
17.3%
3 1516
 
8.1%
9 1467
 
7.8%
5 1182
 
6.3%
4 1047
 
5.6%
7 917
 
4.9%
6 868
 
4.6%
8 864
 
4.6%
Nonspacing Mark
ValueCountFrequency (%)
3
30.0%
3
30.0%
1
 
10.0%
1
 
10.0%
1
 
10.0%
1
 
10.0%
Dash Punctuation
ValueCountFrequency (%)
- 17126
83.1%
3350
 
16.3%
113
 
0.5%
14
 
0.1%
7
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 9308
99.8%
12
 
0.1%
6
 
0.1%
1
 
< 0.1%
[ 1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 9320
99.8%
12
 
0.1%
3
 
< 0.1%
1
 
< 0.1%
Format
ValueCountFrequency (%)
25
61.0%
12
29.3%
­ 3
 
7.3%
1
 
2.4%
Modifier Symbol
ValueCountFrequency (%)
` 109
97.3%
^ 2
 
1.8%
´ 1
 
0.9%
Modifier Letter
ValueCountFrequency (%)
26
92.9%
1
 
3.6%
1
 
3.6%
Currency Symbol
ValueCountFrequency (%)
$ 8
80.0%
1
 
10.0%
¥ 1
 
10.0%
Other Number
ValueCountFrequency (%)
½ 3
42.9%
² 3
42.9%
1
 
14.3%
Space Separator
ValueCountFrequency (%)
1265996
> 99.9%
  7
 
< 0.1%
Final Punctuation
ValueCountFrequency (%)
669
83.6%
131
 
16.4%
Initial Punctuation
ValueCountFrequency (%)
129
83.8%
25
 
16.2%
Control
ValueCountFrequency (%)
29914
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 14
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%
Line Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6162880
80.0%
Common 1540508
 
20.0%
Han 320
 
< 0.1%
Katakana 256
 
< 0.1%
Hiragana 217
 
< 0.1%
Greek 38
 
< 0.1%
Thai 21
 
< 0.1%
Hangul 12
 
< 0.1%
Cyrillic 2
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
7
 
2.2%
7
 
2.2%
6
 
1.9%
5
 
1.6%
5
 
1.6%
5
 
1.6%
4
 
1.2%
4
 
1.2%
4
 
1.2%
4
 
1.2%
Other values (204) 269
84.1%
Common
ValueCountFrequency (%)
1265996
82.2%
, 71567
 
4.6%
. 68725
 
4.5%
29914
 
1.9%
- 17126
 
1.1%
' 15858
 
1.0%
" 11626
 
0.8%
: 9341
 
0.6%
) 9320
 
0.6%
( 9308
 
0.6%
Other values (90) 31727
 
2.1%
Latin
ValueCountFrequency (%)
e 732720
11.9%
a 538240
 
8.7%
t 522334
 
8.5%
i 467905
 
7.6%
o 466981
 
7.6%
n 433271
 
7.0%
s 429811
 
7.0%
r 381011
 
6.2%
h 342660
 
5.6%
l 235183
 
3.8%
Other values (54) 1612764
26.2%
Katakana
ValueCountFrequency (%)
19
 
7.4%
14
 
5.5%
13
 
5.1%
12
 
4.7%
10
 
3.9%
10
 
3.9%
8
 
3.1%
8
 
3.1%
8
 
3.1%
7
 
2.7%
Other values (51) 147
57.4%
Hiragana
ValueCountFrequency (%)
32
 
14.7%
12
 
5.5%
12
 
5.5%
8
 
3.7%
7
 
3.2%
6
 
2.8%
6
 
2.8%
6
 
2.8%
5
 
2.3%
5
 
2.3%
Other values (47) 118
54.4%
Thai
ValueCountFrequency (%)
3
14.3%
3
14.3%
3
14.3%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
Other values (5) 5
23.8%
Hangul
ValueCountFrequency (%)
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
Other values (2) 2
16.7%
Greek
ValueCountFrequency (%)
μ 26
68.4%
δ 3
 
7.9%
ω 2
 
5.3%
θ 1
 
2.6%
ψ 1
 
2.6%
α 1
 
2.6%
γ 1
 
2.6%
β 1
 
2.6%
φ 1
 
2.6%
ε 1
 
2.6%
Cyrillic
ValueCountFrequency (%)
о 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7697743
99.9%
Punctuation 4623
 
0.1%
None 842
 
< 0.1%
CJK 319
 
< 0.1%
Katakana 293
 
< 0.1%
Hiragana 217
 
< 0.1%
Misc Symbols 152
 
< 0.1%
Thai 21
 
< 0.1%
Hangul 12
 
< 0.1%
Geometric Shapes 8
 
< 0.1%
Other values (8) 24
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1265996
16.4%
e 732720
 
9.5%
a 538240
 
7.0%
t 522334
 
6.8%
i 467905
 
6.1%
o 466981
 
6.1%
n 433271
 
5.6%
s 429811
 
5.6%
r 381011
 
4.9%
h 342660
 
4.5%
Other values (53) 2116814
27.5%
Punctuation
ValueCountFrequency (%)
3350
72.5%
669
 
14.5%
131
 
2.8%
130
 
2.8%
129
 
2.8%
113
 
2.4%
25
 
0.5%
25
 
0.5%
14
 
0.3%
12
 
0.3%
Other values (5) 25
 
0.5%
None
ValueCountFrequency (%)
é 421
50.0%
ō 50
 
5.9%
ü 34
 
4.0%
μ 26
 
3.1%
° 25
 
3.0%
è 22
 
2.6%
ä 19
 
2.3%
× 19
 
2.3%
ū 16
 
1.9%
ç 12
 
1.4%
Other values (63) 198
23.5%
Misc Symbols
ValueCountFrequency (%)
100
65.8%
24
 
15.8%
17
 
11.2%
5
 
3.3%
4
 
2.6%
1
 
0.7%
1
 
0.7%
Hiragana
ValueCountFrequency (%)
32
 
14.7%
12
 
5.5%
12
 
5.5%
8
 
3.7%
7
 
3.2%
6
 
2.8%
6
 
2.8%
6
 
2.8%
5
 
2.3%
5
 
2.3%
Other values (47) 118
54.4%
Katakana
ValueCountFrequency (%)
26
 
8.9%
19
 
6.5%
14
 
4.8%
13
 
4.4%
12
 
4.1%
11
 
3.8%
10
 
3.4%
10
 
3.4%
8
 
2.7%
8
 
2.7%
Other values (53) 162
55.3%
CJK
ValueCountFrequency (%)
7
 
2.2%
7
 
2.2%
6
 
1.9%
5
 
1.6%
5
 
1.6%
5
 
1.6%
4
 
1.3%
4
 
1.3%
4
 
1.3%
4
 
1.3%
Other values (203) 268
84.0%
Geometric Shapes
ValueCountFrequency (%)
6
75.0%
1
 
12.5%
1
 
12.5%
Box Drawing
ValueCountFrequency (%)
5
100.0%
Math Operators
ValueCountFrequency (%)
4
66.7%
1
 
16.7%
1
 
16.7%
Thai
ValueCountFrequency (%)
3
14.3%
3
14.3%
3
14.3%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
1
 
4.8%
Other values (5) 5
23.8%
Arrows
ValueCountFrequency (%)
3
60.0%
2
40.0%
Cyrillic
ValueCountFrequency (%)
о 2
100.0%
Hangul
ValueCountFrequency (%)
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
Other values (2) 2
16.7%
Number Forms
ValueCountFrequency (%)
1
50.0%
1
50.0%
Control Pictures
ValueCountFrequency (%)
1
100.0%
Letterlike Symbols
ValueCountFrequency (%)
1
100.0%
Alphabetic PF
ValueCountFrequency (%)
1
50.0%
1
50.0%

type
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size185.7 KiB
tv
6950 
movie
4282 
ova
3977 
ona
3320 
music
2682 
Other values (2)
2537 

Length

Max length7
Median length5
Mean length3.7201449
Min length1

Characters and Unicode

Total characters88346
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtv
2nd rowmovie
3rd rowtv
4th rowtv
5th rowtv

Common Values

ValueCountFrequency (%)
tv 6950
29.3%
movie 4282
18.0%
ova 3977
16.7%
ona 3320
14.0%
music 2682
 
11.3%
special 2533
 
10.7%
- 4
 
< 0.1%

Length

2024-01-01T21:04:52.023867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-01T21:04:52.222901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
tv 6950
29.3%
movie 4282
18.0%
ova 3977
16.7%
ona 3320
14.0%
music 2682
 
11.3%
special 2533
 
10.7%
4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
v 15209
17.2%
o 11579
13.1%
a 9830
11.1%
i 9497
10.7%
m 6964
7.9%
t 6950
7.9%
e 6815
7.7%
s 5215
 
5.9%
c 5215
 
5.9%
n 3320
 
3.8%
Other values (4) 7752
8.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 88342
> 99.9%
Dash Punctuation 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
v 15209
17.2%
o 11579
13.1%
a 9830
11.1%
i 9497
10.8%
m 6964
7.9%
t 6950
7.9%
e 6815
7.7%
s 5215
 
5.9%
c 5215
 
5.9%
n 3320
 
3.8%
Other values (3) 7748
8.8%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 88342
> 99.9%
Common 4
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
v 15209
17.2%
o 11579
13.1%
a 9830
11.1%
i 9497
10.8%
m 6964
7.9%
t 6950
7.9%
e 6815
7.7%
s 5215
 
5.9%
c 5215
 
5.9%
n 3320
 
3.8%
Other values (3) 7748
8.8%
Common
ValueCountFrequency (%)
- 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88346
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
v 15209
17.2%
o 11579
13.1%
a 9830
11.1%
i 9497
10.7%
m 6964
7.9%
t 6950
7.9%
e 6815
7.7%
s 5215
 
5.9%
c 5215
 
5.9%
n 3320
 
3.8%
Other values (4) 7752
8.8%

episodes
Real number (ℝ)

Distinct237
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.796783
Minimum-1
Maximum3057
Zeros0
Zeros (%)0.0%
Negative247
Negative (%)1.0%
Memory size185.7 KiB
2024-01-01T21:04:52.390862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q11
median2
Q313
95-th percentile52
Maximum3057
Range3058
Interquartile range (IQR)12

Descriptive statistics

Standard deviation47.275737
Coefficient of variation (CV)3.4265769
Kurtosis1161.7377
Mean13.796783
Median Absolute Deviation (MAD)1
Skewness26.350075
Sum327646
Variance2234.9953
MonotonicityNot monotonic
2024-01-01T21:04:52.553862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 11348
47.8%
12 1902
 
8.0%
2 1517
 
6.4%
26 1099
 
4.6%
13 965
 
4.1%
52 715
 
3.0%
3 704
 
3.0%
4 542
 
2.3%
6 410
 
1.7%
10 332
 
1.4%
Other values (227) 4214
 
17.7%
ValueCountFrequency (%)
-1 247
 
1.0%
1 11348
47.8%
2 1517
 
6.4%
3 704
 
3.0%
4 542
 
2.3%
5 216
 
0.9%
6 410
 
1.7%
7 125
 
0.5%
8 165
 
0.7%
9 81
 
0.3%
ValueCountFrequency (%)
3057 1
< 0.1%
1818 1
< 0.1%
1787 1
< 0.1%
1664 1
< 0.1%
1565 1
< 0.1%
1471 1
< 0.1%
1428 1
< 0.1%
1306 1
< 0.1%
1274 1
< 0.1%
1006 1
< 0.1%

status
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size185.7 KiB
finished airing
23405 
currently airing
 
343

Length

Max length16
Median length15
Mean length15.014443
Min length15

Characters and Unicode

Total characters356563
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfinished airing
2nd rowfinished airing
3rd rowfinished airing
4th rowfinished airing
5th rowfinished airing

Common Values

ValueCountFrequency (%)
finished airing 23405
98.6%
currently airing 343
 
1.4%

Length

2024-01-01T21:04:52.692866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-01T21:04:52.817866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
airing 23748
50.0%
finished 23405
49.3%
currently 343
 
0.7%

Most occurring characters

ValueCountFrequency (%)
i 94306
26.4%
n 47496
13.3%
r 24434
 
6.9%
e 23748
 
6.7%
23748
 
6.7%
a 23748
 
6.7%
g 23748
 
6.7%
f 23405
 
6.6%
s 23405
 
6.6%
h 23405
 
6.6%
Other values (6) 25120
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 332815
93.3%
Space Separator 23748
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 94306
28.3%
n 47496
14.3%
r 24434
 
7.3%
e 23748
 
7.1%
a 23748
 
7.1%
g 23748
 
7.1%
f 23405
 
7.0%
s 23405
 
7.0%
h 23405
 
7.0%
d 23405
 
7.0%
Other values (5) 1715
 
0.5%
Space Separator
ValueCountFrequency (%)
23748
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 332815
93.3%
Common 23748
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 94306
28.3%
n 47496
14.3%
r 24434
 
7.3%
e 23748
 
7.1%
a 23748
 
7.1%
g 23748
 
7.1%
f 23405
 
7.0%
s 23405
 
7.0%
h 23405
 
7.0%
d 23405
 
7.0%
Other values (5) 1715
 
0.5%
Common
ValueCountFrequency (%)
23748
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 356563
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 94306
26.4%
n 47496
13.3%
r 24434
 
6.9%
e 23748
 
6.7%
23748
 
6.7%
a 23748
 
6.7%
g 23748
 
6.7%
f 23405
 
6.6%
s 23405
 
6.6%
h 23405
 
6.6%
Other values (6) 25120
 
7.0%

producers
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct4351
Distinct (%)18.3%
Missing0
Missing (%)0.0%
Memory size185.7 KiB
-
12442 
nhk
 
716
pink pineapple
 
257
sanrio
 
174
bandai visual
 
124
Other values (4346)
10035 

Length

Max length375
Median length1
Mean length13.747558
Min length1

Characters and Unicode

Total characters326477
Distinct characters51
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3330 ?
Unique (%)14.0%

Sample

1st rowbandai visual
2nd rowsunrise, bandai visual
3rd rowvictor entertainment
4th rowdentsu, bandai visual, tv tokyo music, victor entertainment
5th rowdentsu, tv tokyo

Common Values

ValueCountFrequency (%)
- 12442
52.4%
nhk 716
 
3.0%
pink pineapple 257
 
1.1%
sanrio 174
 
0.7%
bandai visual 124
 
0.5%
fuji tv 120
 
0.5%
tv tokyo 114
 
0.5%
aniplex 114
 
0.5%
gakken 91
 
0.4%
tencent penguin pictures 89
 
0.4%
Other values (4341) 9507
40.0%

Length

2024-01-01T21:04:52.960865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12678
 
22.6%
tv 1319
 
2.3%
entertainment 1095
 
1.9%
pictures 962
 
1.7%
tokyo 943
 
1.7%
nhk 862
 
1.5%
bandai 791
 
1.4%
music 765
 
1.4%
animation 717
 
1.3%
kadokawa 667
 
1.2%
Other values (1708) 35362
63.0%

Most occurring characters

ValueCountFrequency (%)
32413
 
9.9%
a 26603
 
8.1%
i 25387
 
7.8%
n 23941
 
7.3%
o 22036
 
6.7%
t 19400
 
5.9%
e 18854
 
5.8%
s 17981
 
5.5%
- 13288
 
4.1%
, 13207
 
4.0%
Other values (41) 113367
34.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 265754
81.4%
Space Separator 32413
 
9.9%
Other Punctuation 14467
 
4.4%
Dash Punctuation 13288
 
4.1%
Decimal Number 494
 
0.2%
Close Punctuation 18
 
< 0.1%
Open Punctuation 18
 
< 0.1%
Math Symbol 13
 
< 0.1%
Final Punctuation 11
 
< 0.1%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 26603
 
10.0%
i 25387
 
9.6%
n 23941
 
9.0%
o 22036
 
8.3%
t 19400
 
7.3%
e 18854
 
7.1%
s 17981
 
6.8%
r 11755
 
4.4%
u 10735
 
4.0%
c 10560
 
4.0%
Other values (18) 78502
29.5%
Decimal Number
ValueCountFrequency (%)
1 319
64.6%
2 72
 
14.6%
8 36
 
7.3%
0 20
 
4.0%
3 19
 
3.8%
5 15
 
3.0%
7 8
 
1.6%
4 3
 
0.6%
9 2
 
0.4%
Other Punctuation
ValueCountFrequency (%)
, 13207
91.3%
. 807
 
5.6%
& 250
 
1.7%
! 88
 
0.6%
' 85
 
0.6%
/ 23
 
0.2%
: 7
 
< 0.1%
Space Separator
ValueCountFrequency (%)
32413
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13288
100.0%
Close Punctuation
ValueCountFrequency (%)
) 18
100.0%
Open Punctuation
ValueCountFrequency (%)
( 18
100.0%
Math Symbol
ValueCountFrequency (%)
+ 13
100.0%
Final Punctuation
ValueCountFrequency (%)
11
100.0%
Other Symbol
ValueCountFrequency (%)
° 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 265754
81.4%
Common 60723
 
18.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 26603
 
10.0%
i 25387
 
9.6%
n 23941
 
9.0%
o 22036
 
8.3%
t 19400
 
7.3%
e 18854
 
7.1%
s 17981
 
6.8%
r 11755
 
4.4%
u 10735
 
4.0%
c 10560
 
4.0%
Other values (18) 78502
29.5%
Common
ValueCountFrequency (%)
32413
53.4%
- 13288
21.9%
, 13207
21.7%
. 807
 
1.3%
1 319
 
0.5%
& 250
 
0.4%
! 88
 
0.1%
' 85
 
0.1%
2 72
 
0.1%
8 36
 
0.1%
Other values (13) 158
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 326462
> 99.9%
Punctuation 11
 
< 0.1%
None 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
32413
 
9.9%
a 26603
 
8.1%
i 25387
 
7.8%
n 23941
 
7.3%
o 22036
 
6.7%
t 19400
 
5.9%
e 18854
 
5.8%
s 17981
 
5.5%
- 13288
 
4.1%
, 13207
 
4.0%
Other values (37) 113352
34.7%
Punctuation
ValueCountFrequency (%)
11
100.0%
None
ValueCountFrequency (%)
é 2
50.0%
° 1
25.0%
ö 1
25.0%

licensors
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct265
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size185.7 KiB
-
19020 
funimation
 
957
sentai filmworks
 
813
discotek media
 
275
aniplex of america
 
221
Other values (260)
2462 

Length

Max length69
Median length1
Mean length4.2177868
Min length1

Characters and Unicode

Total characters100164
Distinct characters32
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique111 ?
Unique (%)0.5%

Sample

1st rowfunimation, bandai entertainment
2nd rowsony pictures entertainment
3rd rowfunimation, geneon entertainment usa
4th rowfunimation, bandai entertainment
5th rowillumitoon entertainment

Common Values

ValueCountFrequency (%)
- 19020
80.1%
funimation 957
 
4.0%
sentai filmworks 813
 
3.4%
discotek media 275
 
1.2%
aniplex of america 221
 
0.9%
media blasters 207
 
0.9%
viz media 155
 
0.7%
adv films 146
 
0.6%
kitty media 107
 
0.5%
central park media 99
 
0.4%
Other values (255) 1748
 
7.4%

Length

2024-01-01T21:04:53.175900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
19020
64.0%
media 1286
 
4.3%
funimation 1211
 
4.1%
sentai 922
 
3.1%
filmworks 922
 
3.1%
entertainment 711
 
2.4%
discotek 491
 
1.7%
films 328
 
1.1%
adv 305
 
1.0%
america 294
 
1.0%
Other values (122) 4219
 
14.2%

Most occurring characters

ValueCountFrequency (%)
- 19025
19.0%
i 9569
9.6%
n 7901
 
7.9%
a 7896
 
7.9%
e 6944
 
6.9%
t 6192
 
6.2%
5961
 
6.0%
m 5403
 
5.4%
s 4268
 
4.3%
o 4173
 
4.2%
Other values (22) 22832
22.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 74109
74.0%
Dash Punctuation 19025
 
19.0%
Space Separator 5961
 
6.0%
Other Punctuation 1026
 
1.0%
Decimal Number 43
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 9569
12.9%
n 7901
10.7%
a 7896
10.7%
e 6944
9.4%
t 6192
 
8.4%
m 5403
 
7.3%
s 4268
 
5.8%
o 4173
 
5.6%
r 3186
 
4.3%
f 2791
 
3.8%
Other values (16) 15786
21.3%
Other Punctuation
ValueCountFrequency (%)
, 945
92.1%
. 73
 
7.1%
! 8
 
0.8%
Dash Punctuation
ValueCountFrequency (%)
- 19025
100.0%
Space Separator
ValueCountFrequency (%)
5961
100.0%
Decimal Number
ValueCountFrequency (%)
4 43
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 74109
74.0%
Common 26055
 
26.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 9569
12.9%
n 7901
10.7%
a 7896
10.7%
e 6944
9.4%
t 6192
 
8.4%
m 5403
 
7.3%
s 4268
 
5.8%
o 4173
 
5.6%
r 3186
 
4.3%
f 2791
 
3.8%
Other values (16) 15786
21.3%
Common
ValueCountFrequency (%)
- 19025
73.0%
5961
 
22.9%
, 945
 
3.6%
. 73
 
0.3%
4 43
 
0.2%
! 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100164
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 19025
19.0%
i 9569
9.6%
n 7901
 
7.9%
a 7896
 
7.9%
e 6944
 
6.9%
t 6192
 
6.2%
5961
 
6.0%
m 5403
 
5.4%
s 4268
 
4.3%
o 4173
 
4.2%
Other values (22) 22832
22.8%

studios
Categorical

Distinct1518
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size185.7 KiB
-
9671 
toei animation
 
828
sunrise
 
523
j.c.staff
 
381
shanghai animation film studio
 
335
Other values (1513)
12010 

Length

Max length126
Median length98
Mean length7.6955112
Min length1

Characters and Unicode

Total characters182753
Distinct characters50
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique729 ?
Unique (%)3.1%

Sample

1st rowsunrise
2nd rowbones
3rd rowmadhouse
4th rowsunrise
5th rowtoei animation

Common Values

ValueCountFrequency (%)
- 9671
40.7%
toei animation 828
 
3.5%
sunrise 523
 
2.2%
j.c.staff 381
 
1.6%
shanghai animation film studio 335
 
1.4%
madhouse 329
 
1.4%
tms entertainment 313
 
1.3%
studio deen 289
 
1.2%
production i.g 266
 
1.1%
pierrot 262
 
1.1%
Other values (1508) 10551
44.4%

Length

2024-01-01T21:04:53.365862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9717
27.4%
animation 2226
 
6.3%
studio 2053
 
5.8%
toei 859
 
2.4%
production 767
 
2.2%
sunrise 548
 
1.5%
film 543
 
1.5%
entertainment 508
 
1.4%
pictures 453
 
1.3%
j.c.staff 404
 
1.1%
Other values (1217) 17441
49.1%

Most occurring characters

ValueCountFrequency (%)
i 18617
 
10.2%
a 15212
 
8.3%
o 14787
 
8.1%
n 13815
 
7.6%
t 13662
 
7.5%
11771
 
6.4%
e 11266
 
6.2%
- 10494
 
5.7%
s 10269
 
5.6%
m 7090
 
3.9%
Other values (40) 55770
30.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 156124
85.4%
Space Separator 11771
 
6.4%
Dash Punctuation 10494
 
5.7%
Other Punctuation 3534
 
1.9%
Decimal Number 717
 
0.4%
Other Symbol 78
 
< 0.1%
Math Symbol 22
 
< 0.1%
Other Number 13
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 18617
11.9%
a 15212
9.7%
o 14787
 
9.5%
n 13815
 
8.8%
t 13662
 
8.8%
e 11266
 
7.2%
s 10269
 
6.6%
m 7090
 
4.5%
u 7042
 
4.5%
r 7028
 
4.5%
Other values (18) 37336
23.9%
Decimal Number
ValueCountFrequency (%)
1 311
43.4%
9 105
 
14.6%
8 83
 
11.6%
4 78
 
10.9%
2 72
 
10.0%
0 32
 
4.5%
3 22
 
3.1%
7 7
 
1.0%
5 4
 
0.6%
6 3
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 2192
62.0%
, 1103
31.2%
' 121
 
3.4%
& 90
 
2.5%
! 16
 
0.5%
: 9
 
0.3%
3
 
0.1%
Space Separator
ValueCountFrequency (%)
11771
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10494
100.0%
Other Symbol
ValueCountFrequency (%)
° 78
100.0%
Math Symbol
ValueCountFrequency (%)
+ 22
100.0%
Other Number
ValueCountFrequency (%)
² 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 156121
85.4%
Common 26629
 
14.6%
Greek 3
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 18617
11.9%
a 15212
9.7%
o 14787
 
9.5%
n 13815
 
8.8%
t 13662
 
8.8%
e 11266
 
7.2%
s 10269
 
6.6%
m 7090
 
4.5%
u 7042
 
4.5%
r 7028
 
4.5%
Other values (17) 37333
23.9%
Common
ValueCountFrequency (%)
11771
44.2%
- 10494
39.4%
. 2192
 
8.2%
, 1103
 
4.1%
1 311
 
1.2%
' 121
 
0.5%
9 105
 
0.4%
& 90
 
0.3%
8 83
 
0.3%
4 78
 
0.3%
Other values (12) 281
 
1.1%
Greek
ValueCountFrequency (%)
ό 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 182602
99.9%
None 148
 
0.1%
Katakana 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 18617
 
10.2%
a 15212
 
8.3%
o 14787
 
8.1%
n 13815
 
7.6%
t 13662
 
7.5%
11771
 
6.4%
e 11266
 
6.2%
- 10494
 
5.7%
s 10269
 
5.6%
m 7090
 
3.9%
Other values (35) 55619
30.5%
None
ValueCountFrequency (%)
° 78
52.7%
é 54
36.5%
² 13
 
8.8%
ό 3
 
2.0%
Katakana
ValueCountFrequency (%)
3
100.0%

source
Categorical

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size185.7 KiB
original
9018 
manga
4526 
unknown
3603 
game
1205 
visual novel
1098 
Other values (12)
4298 

Length

Max length12
Median length11
Mean length7.2129864
Min length4

Characters and Unicode

Total characters171294
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st roworiginal
2nd roworiginal
3rd rowmanga
4th roworiginal
5th rowmanga

Common Values

ValueCountFrequency (%)
original 9018
38.0%
manga 4526
19.1%
unknown 3603
 
15.2%
game 1205
 
5.1%
visual novel 1098
 
4.6%
other 949
 
4.0%
light novel 885
 
3.7%
novel 673
 
2.8%
web manga 413
 
1.7%
music 393
 
1.7%
Other values (7) 985
 
4.1%

Length

2024-01-01T21:04:53.550862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
original 9018
33.5%
manga 5248
19.5%
unknown 3603
 
13.4%
novel 2722
 
10.1%
game 1273
 
4.7%
visual 1098
 
4.1%
other 949
 
3.5%
light 885
 
3.3%
web 479
 
1.8%
music 393
 
1.5%
Other values (7) 1273
 
4.7%

Most occurring characters

ValueCountFrequency (%)
n 27797
16.2%
a 22426
13.1%
i 20930
12.2%
o 17370
10.1%
g 16424
9.6%
l 13723
8.0%
r 10251
 
6.0%
m 7525
 
4.4%
e 5928
 
3.5%
u 5297
 
3.1%
Other values (14) 23623
13.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 167483
97.8%
Space Separator 3193
 
1.9%
Decimal Number 309
 
0.2%
Dash Punctuation 309
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 27797
16.6%
a 22426
13.4%
i 20930
12.5%
o 17370
10.4%
g 16424
9.8%
l 13723
8.2%
r 10251
 
6.1%
m 7525
 
4.5%
e 5928
 
3.5%
u 5297
 
3.2%
Other values (11) 19812
11.8%
Space Separator
ValueCountFrequency (%)
3193
100.0%
Decimal Number
ValueCountFrequency (%)
4 309
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 309
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 167483
97.8%
Common 3811
 
2.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 27797
16.6%
a 22426
13.4%
i 20930
12.5%
o 17370
10.4%
g 16424
9.8%
l 13723
8.2%
r 10251
 
6.1%
m 7525
 
4.5%
e 5928
 
3.5%
u 5297
 
3.2%
Other values (11) 19812
11.8%
Common
ValueCountFrequency (%)
3193
83.8%
4 309
 
8.1%
- 309
 
8.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 171294
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 27797
16.2%
a 22426
13.1%
i 20930
12.2%
o 17370
10.1%
g 16424
9.6%
l 13723
8.0%
r 10251
 
6.0%
m 7525
 
4.4%
e 5928
 
3.5%
u 5297
 
3.1%
Other values (14) 23623
13.8%

duration
Categorical

Distinct330
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size185.7 KiB
24 min per ep
1952 
23 min per ep
 
1390
2 min
 
1115
3 min
 
1087
4 min
 
947
Other values (325)
17257 

Length

Max length18
Median length17
Mean length9.634664
Min length4

Characters and Unicode

Total characters228804
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)0.2%

Sample

1st row24 min per ep
2nd row1 hr 55 min
3rd row24 min per ep
4th row25 min per ep
5th row23 min per ep

Common Values

ValueCountFrequency (%)
24 min per ep 1952
 
8.2%
23 min per ep 1390
 
5.9%
2 min 1115
 
4.7%
3 min 1087
 
4.6%
4 min 947
 
4.0%
25 min per ep 895
 
3.8%
3 min per ep 587
 
2.5%
5 min per ep 582
 
2.5%
5 min 516
 
2.2%
1 min 494
 
2.1%
Other values (320) 14183
59.7%

Length

2024-01-01T21:04:53.690863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
min 22830
30.3%
per 12068
16.0%
ep 12068
16.0%
1 2992
 
4.0%
24 2315
 
3.1%
hr 2125
 
2.8%
23 1721
 
2.3%
2 1706
 
2.3%
3 1685
 
2.2%
4 1297
 
1.7%
Other values (55) 14633
19.4%

Most occurring characters

ValueCountFrequency (%)
51692
22.6%
e 24731
10.8%
p 24136
10.5%
n 23442
10.2%
m 22830
10.0%
i 22830
10.0%
r 14193
 
6.2%
2 11090
 
4.8%
1 7943
 
3.5%
3 5741
 
2.5%
Other values (14) 20176
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 136089
59.5%
Space Separator 51692
 
22.6%
Decimal Number 40819
 
17.8%
Uppercase Letter 204
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 24731
18.2%
p 24136
17.7%
n 23442
17.2%
m 22830
16.8%
i 22830
16.8%
r 14193
10.4%
h 2125
 
1.6%
s 595
 
0.4%
c 595
 
0.4%
k 204
 
0.1%
Other values (2) 408
 
0.3%
Decimal Number
ValueCountFrequency (%)
2 11090
27.2%
1 7943
19.5%
3 5741
14.1%
4 5113
12.5%
5 3935
 
9.6%
0 2226
 
5.5%
6 1303
 
3.2%
8 1229
 
3.0%
7 1186
 
2.9%
9 1053
 
2.6%
Space Separator
ValueCountFrequency (%)
51692
100.0%
Uppercase Letter
ValueCountFrequency (%)
U 204
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 136293
59.6%
Common 92511
40.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 24731
18.1%
p 24136
17.7%
n 23442
17.2%
m 22830
16.8%
i 22830
16.8%
r 14193
10.4%
h 2125
 
1.6%
s 595
 
0.4%
c 595
 
0.4%
U 204
 
0.1%
Other values (3) 612
 
0.4%
Common
ValueCountFrequency (%)
51692
55.9%
2 11090
 
12.0%
1 7943
 
8.6%
3 5741
 
6.2%
4 5113
 
5.5%
5 3935
 
4.3%
0 2226
 
2.4%
6 1303
 
1.4%
8 1229
 
1.3%
7 1186
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 228804
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
51692
22.6%
e 24731
10.8%
p 24136
10.5%
n 23442
10.2%
m 22830
10.0%
i 22830
10.0%
r 14193
 
6.2%
2 11090
 
4.8%
1 7943
 
3.5%
3 5741
 
2.5%
Other values (14) 20176
 
8.8%

rating
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size185.7 KiB
PG-13 - Teens 13 or older
8313 
G - All Ages
7517 
PG - Children
3541 
Rx - Hentai
1464 
R - 17+ (violence & profanity)
1377 
Other values (2)
1536 

Length

Max length30
Median length25
Mean length17.683468
Min length1

Characters and Unicode

Total characters419947
Distinct characters37
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowR - 17+ (violence & profanity)
2nd rowR - 17+ (violence & profanity)
3rd rowPG-13 - Teens 13 or older
4th rowPG-13 - Teens 13 or older
5th rowPG - Children

Common Values

ValueCountFrequency (%)
PG-13 - Teens 13 or older 8313
35.0%
G - All Ages 7517
31.7%
PG - Children 3541
14.9%
Rx - Hentai 1464
 
6.2%
R - 17+ (violence & profanity) 1377
 
5.8%
R+ - Mild Nudity 1129
 
4.8%
- 407
 
1.7%

Length

2024-01-01T21:04:53.822900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-01T21:04:53.964902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
25125
23.2%
pg-13 8313
 
7.7%
teens 8313
 
7.7%
13 8313
 
7.7%
or 8313
 
7.7%
older 8313
 
7.7%
g 7517
 
7.0%
all 7517
 
7.0%
ages 7517
 
7.0%
children 3541
 
3.3%
Other values (9) 15364
14.2%

Most occurring characters

ValueCountFrequency (%)
84398
20.1%
e 40215
 
9.6%
- 32061
 
7.6%
l 29394
 
7.0%
r 21544
 
5.1%
o 19380
 
4.6%
G 19371
 
4.6%
1 18003
 
4.3%
3 16626
 
4.0%
n 16072
 
3.8%
Other values (27) 122883
29.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 195040
46.4%
Space Separator 84398
20.1%
Uppercase Letter 65805
 
15.7%
Decimal Number 36006
 
8.6%
Dash Punctuation 32061
 
7.6%
Math Symbol 2506
 
0.6%
Close Punctuation 1377
 
0.3%
Open Punctuation 1377
 
0.3%
Other Punctuation 1377
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 40215
20.6%
l 29394
15.1%
r 21544
11.0%
o 19380
9.9%
n 16072
 
8.2%
s 15830
 
8.1%
d 14112
 
7.2%
i 10017
 
5.1%
g 7517
 
3.9%
t 3970
 
2.0%
Other values (9) 16989
8.7%
Uppercase Letter
ValueCountFrequency (%)
G 19371
29.4%
A 15034
22.8%
P 11854
18.0%
T 8313
12.6%
R 3970
 
6.0%
C 3541
 
5.4%
H 1464
 
2.2%
M 1129
 
1.7%
N 1129
 
1.7%
Decimal Number
ValueCountFrequency (%)
1 18003
50.0%
3 16626
46.2%
7 1377
 
3.8%
Space Separator
ValueCountFrequency (%)
84398
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 32061
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2506
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1377
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1377
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1377
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 260845
62.1%
Common 159102
37.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 40215
15.4%
l 29394
11.3%
r 21544
 
8.3%
o 19380
 
7.4%
G 19371
 
7.4%
n 16072
 
6.2%
s 15830
 
6.1%
A 15034
 
5.8%
d 14112
 
5.4%
P 11854
 
4.5%
Other values (18) 58039
22.3%
Common
ValueCountFrequency (%)
84398
53.0%
- 32061
 
20.2%
1 18003
 
11.3%
3 16626
 
10.4%
+ 2506
 
1.6%
) 1377
 
0.9%
( 1377
 
0.9%
& 1377
 
0.9%
7 1377
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 419947
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
84398
20.1%
e 40215
 
9.6%
- 32061
 
7.6%
l 29394
 
7.0%
r 21544
 
5.1%
o 19380
 
4.6%
G 19371
 
4.6%
1 18003
 
4.3%
3 16626
 
4.0%
n 16072
 
3.8%
Other values (27) 122883
29.3%

rank
Real number (ℝ)

Distinct14795
Distinct (%)62.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8073.8259
Minimum-1
Maximum20104
Zeros141
Zeros (%)0.6%
Negative4149
Negative (%)17.5%
Memory size185.7 KiB
2024-01-01T21:04:54.124900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q11648.75
median7583.5
Q313623.25
95-th percentile18808.3
Maximum20104
Range20105
Interquartile range (IQR)11974.5

Descriptive statistics

Standard deviation6461.8198
Coefficient of variation (CV)0.80034174
Kurtosis-1.2685375
Mean8073.8259
Median Absolute Deviation (MAD)5985.5
Skewness0.23666428
Sum1.9173722 × 108
Variance41755116
MonotonicityNot monotonic
2024-01-01T21:04:54.290866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 4149
 
17.5%
0 141
 
0.6%
18459 4
 
< 0.1%
17431 4
 
< 0.1%
6491 4
 
< 0.1%
9635 4
 
< 0.1%
18448 4
 
< 0.1%
15259 4
 
< 0.1%
12881 4
 
< 0.1%
6442 4
 
< 0.1%
Other values (14785) 19426
81.8%
ValueCountFrequency (%)
-1 4149
17.5%
0 141
 
0.6%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
20104 2
< 0.1%
20103 1
< 0.1%
20101 2
< 0.1%
20100 2
< 0.1%
20098 2
< 0.1%
20097 1
< 0.1%
20096 1
< 0.1%
20094 1
< 0.1%
20093 1
< 0.1%
20092 1
< 0.1%

popularity
Real number (ℝ)

Distinct17779
Distinct (%)74.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12051.248
Minimum0
Maximum24717
Zeros141
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size185.7 KiB
2024-01-01T21:04:54.454865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1048.7
Q15949.75
median12063
Q318116.5
95-th percentile23030
Maximum24717
Range24717
Interquartile range (IQR)12166.75

Descriptive statistics

Standard deviation7059.8545
Coefficient of variation (CV)0.58581936
Kurtosis-1.1866251
Mean12051.248
Median Absolute Deviation (MAD)6083
Skewness0.0057210296
Sum2.8619304 × 108
Variance49841545
MonotonicityNot monotonic
2024-01-01T21:04:54.616899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 141
 
0.6%
15563 5
 
< 0.1%
6090 5
 
< 0.1%
22016 5
 
< 0.1%
18993 5
 
< 0.1%
21123 5
 
< 0.1%
21814 5
 
< 0.1%
16280 5
 
< 0.1%
17912 5
 
< 0.1%
21588 5
 
< 0.1%
Other values (17769) 23562
99.2%
ValueCountFrequency (%)
0 141
0.6%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
24717 1
 
< 0.1%
24715 1
 
< 0.1%
24713 1
 
< 0.1%
24710 1
 
< 0.1%
24708 2
< 0.1%
24707 1
 
< 0.1%
24706 3
< 0.1%
24704 2
< 0.1%
24702 2
< 0.1%
24701 1
 
< 0.1%

favorites
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1799
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean451.64338
Minimum0
Maximum217606
Zeros10048
Zeros (%)42.3%
Negative0
Negative (%)0.0%
Memory size185.7 KiB
2024-01-01T21:04:54.783902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q319
95-th percentile892
Maximum217606
Range217606
Interquartile range (IQR)19

Descriptive statistics

Standard deviation4456.7853
Coefficient of variation (CV)9.8679301
Kurtosis958.58548
Mean451.64338
Median Absolute Deviation (MAD)1
Skewness26.37837
Sum10725627
Variance19862935
MonotonicityNot monotonic
2024-01-01T21:04:54.928900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 10048
42.3%
1 2270
 
9.6%
2 1174
 
4.9%
3 780
 
3.3%
4 535
 
2.3%
5 499
 
2.1%
6 340
 
1.4%
7 280
 
1.2%
9 257
 
1.1%
8 255
 
1.1%
Other values (1789) 7310
30.8%
ValueCountFrequency (%)
0 10048
42.3%
1 2270
 
9.6%
2 1174
 
4.9%
3 780
 
3.3%
4 535
 
2.3%
5 499
 
2.1%
6 340
 
1.4%
7 280
 
1.2%
8 255
 
1.1%
9 257
 
1.1%
ValueCountFrequency (%)
217606 1
< 0.1%
200265 1
< 0.1%
198986 1
< 0.1%
182964 1
< 0.1%
167586 1
< 0.1%
163844 1
< 0.1%
107735 1
< 0.1%
105379 1
< 0.1%
100638 1
< 0.1%
88375 1
< 0.1%

scored_by
Real number (ℝ)

Distinct8280
Distinct (%)34.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19805.597
Minimum-1
Maximum2660903
Zeros0
Zeros (%)0.0%
Negative8064
Negative (%)34.0%
Memory size185.7 KiB
2024-01-01T21:04:55.079862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median367.5
Q33747
95-th percentile87169.8
Maximum2660903
Range2660904
Interquartile range (IQR)3748

Descriptive statistics

Standard deviation96106.452
Coefficient of variation (CV)4.8524894
Kurtosis176.07929
Mean19805.597
Median Absolute Deviation (MAD)368.5
Skewness11.25206
Sum4.7034333 × 108
Variance9.23645 × 109
MonotonicityNot monotonic
2024-01-01T21:04:55.237865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 8064
34.0%
128 35
 
0.1%
121 35
 
0.1%
172 33
 
0.1%
150 33
 
0.1%
130 32
 
0.1%
152 31
 
0.1%
134 31
 
0.1%
124 30
 
0.1%
123 30
 
0.1%
Other values (8270) 15394
64.8%
ValueCountFrequency (%)
-1 8064
34.0%
100 1
 
< 0.1%
102 2
 
< 0.1%
103 11
 
< 0.1%
104 11
 
< 0.1%
105 14
 
0.1%
106 17
 
0.1%
107 17
 
0.1%
108 18
 
0.1%
109 18
 
0.1%
ValueCountFrequency (%)
2660903 1
< 0.1%
2619479 1
< 0.1%
2131099 1
< 0.1%
2072240 1
< 0.1%
2020030 1
< 0.1%
1977824 1
< 0.1%
1943121 1
< 0.1%
1883772 1
< 0.1%
1807089 1
< 0.1%
1791023 1
< 0.1%

members
Real number (ℝ)

Distinct10779
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38601.079
Minimum0
Maximum3744541
Zeros139
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size185.7 KiB
2024-01-01T21:04:55.395898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile45
Q1235
median1149.5
Q39796
95-th percentile185010.4
Maximum3744541
Range3744541
Interquartile range (IQR)9561

Descriptive statistics

Standard deviation160377.44
Coefficient of variation (CV)4.1547399
Kurtosis117.4905
Mean38601.079
Median Absolute Deviation (MAD)1095.5
Skewness9.2326254
Sum9.1669842 × 108
Variance2.5720924 × 1010
MonotonicityNot monotonic
2024-01-01T21:04:55.550863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 139
 
0.6%
39 107
 
0.5%
43 101
 
0.4%
41 101
 
0.4%
42 98
 
0.4%
40 96
 
0.4%
38 95
 
0.4%
50 80
 
0.3%
49 78
 
0.3%
51 77
 
0.3%
Other values (10769) 22776
95.9%
ValueCountFrequency (%)
0 139
0.6%
1 1
 
< 0.1%
2 1
 
< 0.1%
15 1
 
< 0.1%
16 1
 
< 0.1%
21 2
 
< 0.1%
22 8
 
< 0.1%
23 8
 
< 0.1%
24 8
 
< 0.1%
25 7
 
< 0.1%
ValueCountFrequency (%)
3744541 1
< 0.1%
3713315 1
< 0.1%
3176556 1
< 0.1%
3058666 1
< 0.1%
2951821 1
< 0.1%
2882333 1
< 0.1%
2808712 1
< 0.1%
2717330 1
< 0.1%
2699241 1
< 0.1%
2656870 1
< 0.1%

image_url
Categorical

Distinct23590
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size185.7 KiB
https://cdn.myanimelist.net/img/sp/icon/apple-touch-icon-256.png
 
159
https://cdn.myanimelist.net/images/anime/4/19644.jpg
 
1
https://cdn.myanimelist.net/images/anime/1993/102813.jpg
 
1
https://cdn.myanimelist.net/images/anime/1592/128270.jpg
 
1
https://cdn.myanimelist.net/images/anime/1778/119276.jpg
 
1
Other values (23585)
23585 

Length

Max length64
Median length56
Mean length54.29552
Min length49

Characters and Unicode

Total characters1289410
Distinct characters31
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23589 ?
Unique (%)99.3%

Sample

1st rowhttps://cdn.myanimelist.net/images/anime/4/19644.jpg
2nd rowhttps://cdn.myanimelist.net/images/anime/1439/93480.jpg
3rd rowhttps://cdn.myanimelist.net/images/anime/7/20310.jpg
4th rowhttps://cdn.myanimelist.net/images/anime/10/19969.jpg
5th rowhttps://cdn.myanimelist.net/images/anime/7/21569.jpg

Common Values

ValueCountFrequency (%)
https://cdn.myanimelist.net/img/sp/icon/apple-touch-icon-256.png 159
 
0.7%
https://cdn.myanimelist.net/images/anime/4/19644.jpg 1
 
< 0.1%
https://cdn.myanimelist.net/images/anime/1993/102813.jpg 1
 
< 0.1%
https://cdn.myanimelist.net/images/anime/1592/128270.jpg 1
 
< 0.1%
https://cdn.myanimelist.net/images/anime/1778/119276.jpg 1
 
< 0.1%
https://cdn.myanimelist.net/images/anime/1806/105775.jpg 1
 
< 0.1%
https://cdn.myanimelist.net/images/anime/1806/124660.jpg 1
 
< 0.1%
https://cdn.myanimelist.net/images/anime/1191/114788.jpg 1
 
< 0.1%
https://cdn.myanimelist.net/images/anime/1034/102820.jpg 1
 
< 0.1%
https://cdn.myanimelist.net/images/anime/1145/104707.jpg 1
 
< 0.1%
Other values (23580) 23580
99.3%

Length

2024-01-01T21:04:55.899899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://cdn.myanimelist.net/img/sp/icon/apple-touch-icon-256.png 159
 
0.7%
https://cdn.myanimelist.net/images/anime/1146/124743.jpg 1
 
< 0.1%
https://cdn.myanimelist.net/images/anime/7/21569.jpg 1
 
< 0.1%
https://cdn.myanimelist.net/images/anime/1079/133529.jpg 1
 
< 0.1%
https://cdn.myanimelist.net/images/anime/1301/133577.jpg 1
 
< 0.1%
https://cdn.myanimelist.net/images/anime/12/49655.jpg 1
 
< 0.1%
https://cdn.myanimelist.net/images/anime/9/10521.jpg 1
 
< 0.1%
https://cdn.myanimelist.net/images/anime/10/18793.jpg 1
 
< 0.1%
https://cdn.myanimelist.net/images/anime/13/17405.jpg 1
 
< 0.1%
https://cdn.myanimelist.net/images/anime/6/73245.jpg 1
 
< 0.1%
Other values (23580) 23580
99.3%

Most occurring characters

ValueCountFrequency (%)
/ 142488
 
11.1%
n 95310
 
7.4%
t 95151
 
7.4%
i 95151
 
7.4%
e 94833
 
7.4%
m 94833
 
7.4%
s 71244
 
5.5%
. 71244
 
5.5%
a 71085
 
5.5%
p 47973
 
3.7%
Other values (21) 410098
31.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 856836
66.5%
Other Punctuation 237480
 
18.4%
Decimal Number 194617
 
15.1%
Dash Punctuation 477
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 95310
11.1%
t 95151
11.1%
i 95151
11.1%
e 94833
11.1%
m 94833
11.1%
s 71244
8.3%
a 71085
8.3%
p 47973
5.6%
g 47496
 
5.5%
c 24225
 
2.8%
Other values (7) 119535
14.0%
Decimal Number
ValueCountFrequency (%)
1 45957
23.6%
2 19982
10.3%
3 18411
9.5%
7 16679
 
8.6%
9 16430
 
8.4%
8 16329
 
8.4%
5 15894
 
8.2%
0 15486
 
8.0%
6 14962
 
7.7%
4 14487
 
7.4%
Other Punctuation
ValueCountFrequency (%)
/ 142488
60.0%
. 71244
30.0%
: 23748
 
10.0%
Dash Punctuation
ValueCountFrequency (%)
- 477
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 856836
66.5%
Common 432574
33.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 95310
11.1%
t 95151
11.1%
i 95151
11.1%
e 94833
11.1%
m 94833
11.1%
s 71244
8.3%
a 71085
8.3%
p 47973
5.6%
g 47496
 
5.5%
c 24225
 
2.8%
Other values (7) 119535
14.0%
Common
ValueCountFrequency (%)
/ 142488
32.9%
. 71244
16.5%
1 45957
 
10.6%
: 23748
 
5.5%
2 19982
 
4.6%
3 18411
 
4.3%
7 16679
 
3.9%
9 16430
 
3.8%
8 16329
 
3.8%
5 15894
 
3.7%
Other values (4) 45412
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1289410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 142488
 
11.1%
n 95310
 
7.4%
t 95151
 
7.4%
i 95151
 
7.4%
e 94833
 
7.4%
m 94833
 
7.4%
s 71244
 
5.5%
. 71244
 
5.5%
a 71085
 
5.5%
p 47973
 
3.7%
Other values (21) 410098
31.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size185.7 KiB
0
23507 
1
 
241

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23748
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23507
99.0%
1 241
 
1.0%

Length

2024-01-01T21:04:56.027864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-01T21:04:56.157866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 23507
99.0%
1 241
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 23507
99.0%
1 241
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23748
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23507
99.0%
1 241
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23748
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23507
99.0%
1 241
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23507
99.0%
1 241
 
1.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size185.7 KiB
0
22016 
1
 
1732

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23748
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 22016
92.7%
1 1732
 
7.3%

Length

2024-01-01T21:04:56.258864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-01T21:04:56.380862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 22016
92.7%
1 1732
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 22016
92.7%
1 1732
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23748
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22016
92.7%
1 1732
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common 23748
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22016
92.7%
1 1732
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22016
92.7%
1 1732
 
7.3%

genre_fantasy
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size185.7 KiB
0
18793 
1
4955 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23748
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 18793
79.1%
1 4955
 
20.9%

Length

2024-01-01T21:04:56.466863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-01T21:04:56.587888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 18793
79.1%
1 4955
 
20.9%

Most occurring characters

ValueCountFrequency (%)
0 18793
79.1%
1 4955
 
20.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23748
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18793
79.1%
1 4955
 
20.9%

Most occurring scripts

ValueCountFrequency (%)
Common 23748
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18793
79.1%
1 4955
 
20.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18793
79.1%
1 4955
 
20.9%

genre_sci_fi
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size185.7 KiB
0
20736 
1
3012 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23748
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20736
87.3%
1 3012
 
12.7%

Length

2024-01-01T21:04:56.690866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-01T21:04:56.809906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 20736
87.3%
1 3012
 
12.7%

Most occurring characters

ValueCountFrequency (%)
0 20736
87.3%
1 3012
 
12.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23748
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20736
87.3%
1 3012
 
12.7%

Most occurring scripts

ValueCountFrequency (%)
Common 23748
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20736
87.3%
1 3012
 
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20736
87.3%
1 3012
 
12.7%

genre_erotica
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size185.7 KiB
0
23695 
1
 
53

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23748
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23695
99.8%
1 53
 
0.2%

Length

2024-01-01T21:04:56.898865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-01T21:04:57.008866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 23695
99.8%
1 53
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 23695
99.8%
1 53
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23748
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23695
99.8%
1 53
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 23748
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23695
99.8%
1 53
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23695
99.8%
1 53
 
0.2%

genre_romance
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size185.7 KiB
0
21766 
1
 
1982

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23748
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21766
91.7%
1 1982
 
8.3%

Length

2024-01-01T21:04:57.101866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-01T21:04:57.221862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 21766
91.7%
1 1982
 
8.3%

Most occurring characters

ValueCountFrequency (%)
0 21766
91.7%
1 1982
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23748
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21766
91.7%
1 1982
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
Common 23748
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21766
91.7%
1 1982
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21766
91.7%
1 1982
 
8.3%

genre_horror
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size185.7 KiB
0
23223 
1
 
525

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23748
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23223
97.8%
1 525
 
2.2%

Length

2024-01-01T21:04:57.331866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-01T21:04:57.439865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 23223
97.8%
1 525
 
2.2%

Most occurring characters

ValueCountFrequency (%)
0 23223
97.8%
1 525
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23748
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23223
97.8%
1 525
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Common 23748
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23223
97.8%
1 525
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23223
97.8%
1 525
 
2.2%

genre_boys_love
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size185.7 KiB
0
23587 
1
 
161

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23748
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23587
99.3%
1 161
 
0.7%

Length

2024-01-01T21:04:57.542869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-01T21:04:57.657866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 23587
99.3%
1 161
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 23587
99.3%
1 161
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23748
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23587
99.3%
1 161
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 23748
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23587
99.3%
1 161
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23587
99.3%
1 161
 
0.7%

genre_girls_love
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size185.7 KiB
0
23642 
1
 
106

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23748
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23642
99.6%
1 106
 
0.4%

Length

2024-01-01T21:04:57.754863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-01T21:04:57.879899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 23642
99.6%
1 106
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 23642
99.6%
1 106
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23748
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23642
99.6%
1 106
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 23748
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23642
99.6%
1 106
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23642
99.6%
1 106
 
0.4%

genre_sports
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size185.7 KiB
0
23005 
1
 
743

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23748
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23005
96.9%
1 743
 
3.1%

Length

2024-01-01T21:04:57.967866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-01T21:04:58.095903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 23005
96.9%
1 743
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 23005
96.9%
1 743
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23748
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23005
96.9%
1 743
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Common 23748
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23005
96.9%
1 743
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23005
96.9%
1 743
 
3.1%

genre_comedy
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size185.7 KiB
0
16792 
1
6956 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23748
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 16792
70.7%
1 6956
29.3%

Length

2024-01-01T21:04:58.187899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-01T21:04:58.300863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 16792
70.7%
1 6956
29.3%

Most occurring characters

ValueCountFrequency (%)
0 16792
70.7%
1 6956
29.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23748
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 16792
70.7%
1 6956
29.3%

Most occurring scripts

ValueCountFrequency (%)
Common 23748
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 16792
70.7%
1 6956
29.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 16792
70.7%
1 6956
29.3%

genre__
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size185.7 KiB
0
19243 
1
4505 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23748
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19243
81.0%
1 4505
 
19.0%

Length

2024-01-01T21:04:58.393865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-01T21:04:58.502861image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 19243
81.0%
1 4505
 
19.0%

Most occurring characters

ValueCountFrequency (%)
0 19243
81.0%
1 4505
 
19.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23748
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19243
81.0%
1 4505
 
19.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23748
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19243
81.0%
1 4505
 
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19243
81.0%
1 4505
 
19.0%

genre_gourmet
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size185.7 KiB
0
23607 
1
 
141

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23748
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23607
99.4%
1 141
 
0.6%

Length

2024-01-01T21:04:58.595865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-01T21:04:58.703865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 23607
99.4%
1 141
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 23607
99.4%
1 141
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23748
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23607
99.4%
1 141
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 23748
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23607
99.4%
1 141
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23607
99.4%
1 141
 
0.6%

genre_suspense
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size185.7 KiB
0
23519 
1
 
229

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23748
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23519
99.0%
1 229
 
1.0%

Length

2024-01-01T21:04:58.799899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-01T21:04:58.907862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 23519
99.0%
1 229
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 23519
99.0%
1 229
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23748
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23519
99.0%
1 229
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23748
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23519
99.0%
1 229
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23519
99.0%
1 229
 
1.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size185.7 KiB
0
22315 
1
 
1433

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23748
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 22315
94.0%
1 1433
 
6.0%

Length

2024-01-01T21:04:58.995862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-01T21:04:59.132863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 22315
94.0%
1 1433
 
6.0%

Most occurring characters

ValueCountFrequency (%)
0 22315
94.0%
1 1433
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23748
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22315
94.0%
1 1433
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23748
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22315
94.0%
1 1433
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22315
94.0%
1 1433
 
6.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size185.7 KiB
0
22950 
1
 
798

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23748
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 22950
96.6%
1 798
 
3.4%

Length

2024-01-01T21:04:59.221902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-01T21:04:59.333901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 22950
96.6%
1 798
 
3.4%

Most occurring characters

ValueCountFrequency (%)
0 22950
96.6%
1 798
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23748
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22950
96.6%
1 798
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Common 23748
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22950
96.6%
1 798
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22950
96.6%
1 798
 
3.4%

genre_hentai
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size185.7 KiB
0
22273 
1
 
1475

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23748
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 22273
93.8%
1 1475
 
6.2%

Length

2024-01-01T21:04:59.420862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-01T21:04:59.526901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 22273
93.8%
1 1475
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0 22273
93.8%
1 1475
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23748
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22273
93.8%
1 1475
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Common 23748
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22273
93.8%
1 1475
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22273
93.8%
1 1475
 
6.2%

genre_drama
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size185.7 KiB
0
21006 
1
2742 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23748
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 21006
88.5%
1 2742
 
11.5%

Length

2024-01-01T21:04:59.618905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-01T21:04:59.724898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 21006
88.5%
1 2742
 
11.5%

Most occurring characters

ValueCountFrequency (%)
0 21006
88.5%
1 2742
 
11.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23748
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21006
88.5%
1 2742
 
11.5%

Most occurring scripts

ValueCountFrequency (%)
Common 23748
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21006
88.5%
1 2742
 
11.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21006
88.5%
1 2742
 
11.5%

genre_mystery
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size185.7 KiB
0
22921 
1
 
827

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23748
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 22921
96.5%
1 827
 
3.5%

Length

2024-01-01T21:04:59.821862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-01T21:04:59.928861image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 22921
96.5%
1 827
 
3.5%

Most occurring characters

ValueCountFrequency (%)
0 22921
96.5%
1 827
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23748
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22921
96.5%
1 827
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 23748
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22921
96.5%
1 827
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22921
96.5%
1 827
 
3.5%

genre_adventure
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size185.7 KiB
0
20044 
1
3704 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23748
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 20044
84.4%
1 3704
 
15.6%

Length

2024-01-01T21:05:00.019897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-01T21:05:00.135898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 20044
84.4%
1 3704
 
15.6%

Most occurring characters

ValueCountFrequency (%)
0 20044
84.4%
1 3704
 
15.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23748
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 20044
84.4%
1 3704
 
15.6%

Most occurring scripts

ValueCountFrequency (%)
Common 23748
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 20044
84.4%
1 3704
 
15.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 20044
84.4%
1 3704
 
15.6%

genre_ecchi
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size185.7 KiB
0
22965 
1
 
783

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23748
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 22965
96.7%
1 783
 
3.3%

Length

2024-01-01T21:05:00.228902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-01T21:05:00.339899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 22965
96.7%
1 783
 
3.3%

Most occurring characters

ValueCountFrequency (%)
0 22965
96.7%
1 783
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23748
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 22965
96.7%
1 783
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 23748
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 22965
96.7%
1 783
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 22965
96.7%
1 783
 
3.3%

genre_action
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size185.7 KiB
0
19211 
1
4537 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters23748
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 19211
80.9%
1 4537
 
19.1%

Length

2024-01-01T21:05:00.426862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-01T21:05:00.534862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 19211
80.9%
1 4537
 
19.1%

Most occurring characters

ValueCountFrequency (%)
0 19211
80.9%
1 4537
 
19.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 23748
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19211
80.9%
1 4537
 
19.1%

Most occurring scripts

ValueCountFrequency (%)
Common 23748
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19211
80.9%
1 4537
 
19.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19211
80.9%
1 4537
 
19.1%

Interactions

2024-01-01T21:04:46.994913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:39.658802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:40.706805image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:41.710840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:42.886863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:43.952863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:44.979866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:45.968863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:47.130864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:39.792804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:40.836805image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:41.833862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:43.019862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:44.077863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:45.099863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:46.103863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:47.253863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:39.906803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:40.949801image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:41.950863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:43.146864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:44.193863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:45.213899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:46.220863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:47.385863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:40.056802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:41.075801image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:42.086901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:43.283863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:44.318864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:45.345868image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:46.346864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:47.533865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:40.191801image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:41.213802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:42.224863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:43.424862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:44.466864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:45.479862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:46.490865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:47.667901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:40.313837image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:41.334802image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:42.348905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:43.553863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:44.585879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:45.599869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:46.615866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:47.793863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:40.438807image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:41.447803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:42.472866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:43.678861image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:44.703867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:45.716862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:46.737899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:47.922899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:40.570814image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:41.569801image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:42.747862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:43.811905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:44.829865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:45.838900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2024-01-01T21:04:46.858863image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2024-01-01T21:05:00.678899image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
idscoreepisodesrankpopularityfavoritesscored_bymemberstypestatussourceratinggenre_award_winninggenre_slice_of_lifegenre_fantasygenre_sci_figenre_eroticagenre_romancegenre_horrorgenre_boys_lovegenre_girls_lovegenre_sportsgenre_comedygenre__genre_gourmetgenre_suspensegenre_supernaturalgenre_avant_gardegenre_hentaigenre_dramagenre_mysterygenre_adventuregenre_ecchigenre_action
id1.000-0.479-0.0650.2210.540-0.430-0.529-0.5740.2290.1860.1680.2210.0790.0970.1390.2340.0300.1830.0790.0340.0340.0620.1610.3090.0200.0220.0880.1470.1750.1970.0780.1880.1280.184
score-0.4791.0000.097-0.633-0.8610.8400.9150.8850.1520.0600.2020.2510.1620.0230.0980.1520.0210.2330.0880.0460.0500.0740.1390.3110.0260.1130.1630.2400.2220.2050.1770.1360.1480.274
episodes-0.0650.0971.0000.152-0.0900.2150.0970.1070.0250.0090.0000.0250.0000.0100.0000.0070.0000.0000.0000.0000.0000.0000.0220.0160.0000.0000.0000.0000.0000.0000.0000.0070.0000.004
rank0.221-0.6330.1521.0000.538-0.503-0.577-0.5150.2860.0610.1860.2750.0570.0800.1470.1880.0760.2150.0680.0460.0400.0530.2450.3070.0250.0300.1250.1590.4280.1360.1170.1750.1840.267
popularity0.540-0.861-0.0900.5381.000-0.883-0.939-0.9650.2340.0380.2260.3310.1120.0950.1830.1780.0460.2810.0820.0940.0760.0690.1790.4060.0260.0950.1880.1310.3320.1910.1680.1040.2080.307
favorites-0.4300.8400.215-0.503-0.8831.0000.8900.9030.0350.0260.0270.0430.1590.0000.0210.0180.0000.0250.0110.0000.0000.0000.0130.0280.0000.1370.0620.0000.0030.0570.0430.0300.0000.062
scored_by-0.5290.9150.097-0.577-0.9390.8901.0000.9630.0670.0000.0660.0710.1170.0200.0510.0000.0000.0980.0310.0000.0000.0000.0250.0590.0000.1210.0730.0000.0270.0600.0480.0310.0560.105
members-0.5740.8850.107-0.515-0.9650.9030.9631.0000.0830.0000.0780.0870.1220.0200.0580.0150.0000.1260.0340.0000.0000.0000.0290.0740.0000.1380.0910.0000.0360.0700.0850.0240.0790.113
type0.2290.1520.0250.2860.2340.0350.0670.0831.0000.1230.2620.2800.1390.1260.1810.1210.0550.1010.0520.0600.0350.0820.2810.5190.0490.0350.0750.2260.5310.1420.0720.1920.1370.156
status0.1860.0600.0090.0610.0380.0260.0000.0000.1231.0000.1170.0590.0030.0140.0360.0190.0000.0000.0160.0040.0000.0080.0260.0340.0000.0050.0000.0210.0290.0280.0060.0000.0090.022
source0.1680.2020.0000.1860.2260.0270.0660.0780.2620.1171.0000.3030.0600.1900.2350.0870.0440.2550.0600.1060.0650.1300.3020.3500.0420.0510.1700.1840.5460.1500.1350.1880.2070.246
rating0.2210.2510.0250.2750.3310.0430.0710.0870.2800.0590.3031.0000.0430.1510.1980.2060.1620.2470.2550.0770.0630.0780.2160.3240.0430.2170.2120.1330.9950.1930.2350.1610.4410.383
genre_award_winning0.0790.1620.0000.0570.1120.1590.1170.1220.1390.0030.0600.0431.0000.0000.0000.0370.0000.0140.0000.0000.0000.0030.0100.0480.0000.0390.0180.0110.0240.1140.0390.0000.0110.000
genre_slice_of_life0.0970.0230.0100.0800.0950.0000.0200.0200.1260.0140.1900.1510.0001.0000.0890.0690.0000.0310.0400.0000.0000.0330.0750.1350.0490.0230.0170.0450.0700.0040.0350.0840.0270.126
genre_fantasy0.1390.0980.0000.1470.1830.0210.0510.0580.1810.0360.2350.1980.0000.0891.0000.0720.0150.0000.0000.0180.0170.0800.0320.2480.0130.0280.0140.0790.0940.0470.0210.2970.0120.155
genre_sci_fi0.2340.1520.0070.1880.1780.0180.0000.0150.1210.0190.0870.2060.0370.0690.0721.0000.0090.0170.0140.0120.0070.0330.0200.1840.0260.0260.0450.0520.0820.0710.0200.1270.0140.301
genre_erotica0.0300.0210.0000.0760.0460.0000.0000.0000.0550.0000.0440.1620.0000.0000.0150.0091.0000.0350.0000.3280.0000.0000.0000.0210.0000.0000.0000.0170.0080.0540.0010.0150.0000.014
genre_romance0.1830.2330.0000.2150.2810.0250.0980.1260.1010.0000.2550.2470.0140.0310.0000.0170.0351.0000.0160.0760.0400.0190.0950.1460.0000.0000.0530.0450.0490.2000.0000.0130.1610.010
genre_horror0.0790.0880.0000.0680.0820.0110.0310.0340.0520.0160.0600.2550.0000.0400.0000.0140.0000.0161.0000.0000.0000.0240.0410.0720.0070.1390.2260.0440.0000.0260.1140.0120.0090.045
genre_boys_love0.0340.0460.0000.0460.0940.0000.0000.0000.0600.0040.1060.0770.0000.0000.0180.0120.3280.0760.0001.0000.0000.0000.0120.0390.0000.0000.0180.0090.0040.0800.0000.0240.0120.017
genre_girls_love0.0340.0500.0000.0400.0760.0000.0000.0000.0350.0000.0650.0630.0000.0000.0170.0070.0000.0400.0000.0001.0000.0080.0190.0310.0000.0000.0000.0030.0200.0230.0040.0250.0460.000
genre_sports0.0620.0740.0000.0530.0690.0000.0000.0000.0820.0080.1300.0780.0030.0330.0800.0330.0000.0190.0240.0000.0081.0000.0170.0860.0060.0120.0410.0290.0410.0000.0270.0510.0020.040
genre_comedy0.1610.1390.0220.2450.1790.0130.0250.0290.2810.0260.3020.2160.0100.0750.0320.0200.0000.0950.0410.0120.0190.0171.0000.3110.0410.0540.0130.0960.1430.1050.0000.0110.1790.050
genre__0.3090.3110.0160.3070.4060.0280.0590.0740.5190.0340.3500.3240.0480.1350.2480.1840.0210.1460.0720.0390.0310.0860.3111.0000.0360.0470.1220.0900.1240.1750.0910.2080.0890.235
genre_gourmet0.0200.0260.0000.0250.0260.0000.0000.0000.0490.0000.0420.0430.0000.0490.0130.0260.0000.0000.0070.0000.0000.0060.0410.0361.0000.0000.0100.0110.0180.0120.0080.0070.0060.022
genre_suspense0.0220.1130.0000.0300.0950.1370.1210.1380.0350.0050.0510.2170.0390.0230.0280.0260.0000.0000.1390.0000.0000.0120.0540.0470.0001.0000.0730.0120.0180.0640.1610.0210.0160.026
genre_supernatural0.0880.1630.0000.1250.1880.0620.0730.0910.0750.0000.1700.2120.0180.0170.0140.0450.0000.0530.2260.0180.0000.0410.0130.1220.0100.0731.0000.0230.0040.0430.1650.0060.0340.092
genre_avant_garde0.1470.2400.0000.1590.1310.0000.0000.0000.2260.0210.1840.1330.0110.0450.0790.0520.0170.0450.0440.0090.0030.0290.0960.0900.0110.0120.0231.0000.0460.0370.0180.0760.0260.081
genre_hentai0.1750.2220.0000.4280.3320.0030.0270.0360.5310.0290.5460.9950.0240.0700.0940.0820.0080.0490.0000.0040.0200.0410.1430.1240.0180.0180.0040.0461.0000.0760.0420.1030.0470.106
genre_drama0.1970.2050.0000.1360.1910.0570.0600.0700.1420.0280.1500.1930.1140.0040.0470.0710.0540.2000.0260.0800.0230.0000.1050.1750.0120.0640.0430.0370.0761.0000.0540.0050.0290.043
genre_mystery0.0780.1770.0000.1170.1680.0430.0480.0850.0720.0060.1350.2350.0390.0350.0210.0200.0010.0000.1140.0000.0040.0270.0000.0910.0080.1610.1650.0180.0420.0541.0000.0450.0160.081
genre_adventure0.1880.1360.0070.1750.1040.0300.0310.0240.1920.0000.1880.1610.0000.0840.2970.1270.0150.0130.0120.0240.0250.0510.0110.2080.0070.0210.0060.0760.1030.0050.0451.0000.0270.237
genre_ecchi0.1280.1480.0000.1840.2080.0000.0560.0790.1370.0090.2070.4410.0110.0270.0120.0140.0000.1610.0090.0120.0460.0020.1790.0890.0060.0160.0340.0260.0470.0290.0160.0271.0000.048
genre_action0.1840.2740.0040.2670.3070.0620.1050.1130.1560.0220.2460.3830.0000.1260.1550.3010.0140.0100.0450.0170.0000.0400.0500.2350.0220.0260.0920.0810.1060.0430.0810.2370.0481.000

Missing values

2024-01-01T21:04:48.433866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-01T21:04:49.575865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idtitlescoregenressynopsistypeepisodesstatusproducerslicensorsstudiossourcedurationratingrankpopularityfavoritesscored_bymembersimage_urlgenre_award_winninggenre_slice_of_lifegenre_fantasygenre_sci_figenre_eroticagenre_romancegenre_horrorgenre_boys_lovegenre_girls_lovegenre_sportsgenre_comedygenre__genre_gourmetgenre_suspensegenre_supernaturalgenre_avant_gardegenre_hentaigenre_dramagenre_mysterygenre_adventuregenre_ecchigenre_action
01cowboy bebop8.75['award winning', 'action', 'sci-fi']crime is timeless. by the year 2071, humanity has expanded across the galaxy, filling the surface of other planets with settlements like those on earth. these new societies are plagued by murder, drug use, and theft, and intergalactic outlaws are hunted by a growing number of tough bounty hunters.\n\nspike spiegel and jet black pursue criminals throughout space to make a humble living. beneath his goofy and aloof demeanor, spike is haunted by the weight of his violent past. meanwhile, jet manages his own troubled memories while taking care of spike and the bebop, their ship. the duo is joined by the beautiful con artist faye valentine, odd child edward wong hau pepelu tivrusky iv, and ein, a bioengineered welsh corgi.\n\nwhile developing bonds and working to catch a colorful cast of criminals, the bebop crew's lives are disrupted by a menace from spike's past. as a rival's maniacal plot continues to unravel, spike must choose between life with his newfound family or revenge for his old wounds.tv26finished airingbandai visualfunimation, bandai entertainmentsunriseoriginal24 min per epR - 17+ (violence & profanity)4143785259141931771505https://cdn.myanimelist.net/images/anime/4/19644.jpg1001000000000000000001
15cowboy bebop tengoku no tobira8.38['action', 'sci-fi']another day, another bounty—such is the life of the often unlucky crew of the bebop. however, this routine is interrupted when faye, who is chasing a fairly worthless target on mars, witnesses an oil tanker suddenly explode, causing mass hysteria. as casualties mount due to a strange disease spreading through the smoke from the blast, a whopping three hundred million woolong price is placed on the head of the supposed perpetrator.\n\nwith lives at stake and a solution to their money problems in sight, the bebop crew springs into action. spike, jet, faye, and edward, followed closely by ein, split up to pursue different leads across alba city. through their individual investigations, they discover a cover-up scheme involving a pharmaceutical company, revealing a plot that reaches much further than the ragtag team of bounty hunters could have realized.movie1finished airingsunrise, bandai visualsony pictures entertainmentbonesoriginal1 hr 55 minR - 17+ (violence & profanity)1896021448206248360978https://cdn.myanimelist.net/images/anime/1439/93480.jpg0001000000000000000001
26trigun8.22['adventure', 'action', 'sci-fi']vash the stampede is the man with a $$60,000,000,000 bounty on his head. the reason: he's a merciless villain who lays waste to all those that oppose him and flattens entire cities for fun, garnering him the title "the humanoid typhoon." he leaves a trail of death and destruction wherever he goes, and anyone can count themselves dead if they so much as make eye contact—or so the rumors say. in actuality, vash is a huge softie who claims to have never taken a life and avoids violence at all costs.\n\nwith his crazy doughnut obsession and buffoonish attitude in tow, vash traverses the wasteland of the planet gunsmoke, all the while followed by two insurance agents, meryl stryfe and milly thompson, who attempt to minimize his impact on the public. but soon, their misadventures evolve into life-or-death situations as a group of legendary assassins are summoned to bring about suffering to the trio. vash's agonizing past will be unraveled and his morality and principles pushed to the breaking point.tv26finished airingvictor entertainmentfunimation, geneon entertainment usamadhousemanga24 min per epPG-13 - Teens 13 or older32824615035356739727252https://cdn.myanimelist.net/images/anime/7/20310.jpg0001000000000000000101
37witch hunter robin7.25['mystery', 'supernatural', 'action', 'drama']robin sena is a powerful craft user drafted into the stnj—a group of specialized hunters that fight deadly beings known as witches. though her fire power is great, she's got a lot to learn about her powers and working with her cool and aloof partner, amon. but the truth about the witches and herself will leave robin on an entirely new path that she never expected!\n\n(source: funimation)tv26finished airingdentsu, bandai visual, tv tokyo music, victor entertainmentfunimation, bandai entertainmentsunriseoriginal25 min per epPG-13 - Teens 13 or older2764179561342829111931https://cdn.myanimelist.net/images/anime/10/19969.jpg0000000000000010011001
48bouken ou beet6.94['adventure', 'supernatural', 'fantasy']it is the dark century and the people are suffering under the rule of the devil, vandel, who is able to manipulate monsters. the vandel busters are a group of people who hunt these devils, and among them, the zenon squad is known to be the strongest busters on the continent. a young boy, beet, dreams of joining the zenon squad. however, one day, as a result of beet's fault, the zenon squad was defeated by the devil, beltose. the five dying busters sacrificed their life power into their five weapons, saiga. after giving their weapons to beet, they passed away. years have passed since then and the young vandel buster, beet, begins his adventure to carry out the zenon squad's will to put an end to the dark century.tv52finished airingdentsu, tv tokyoillumitoon entertainmenttoei animationmanga23 min per epPG - Children4240512614641315001https://cdn.myanimelist.net/images/anime/7/21569.jpg0010000000000010000100
515eyeshield 217.92['sports']shy, reserved, and small-statured, deimon high school student sena kobayakawa is the perfect target for bullies. however, as a result of running errands throughout his life, sena has become agile and developed a skill for avoiding crowds of people. after the cunning youichi hiruma—captain of the deimon devil bats football team—witnesses sena's rapid legs in motion, he coerces the timid boy into joining his squad.\n\nas hiruma wants to conceal sena's identity from other clubs, sena is forced to hide under the visored helmet of "eyeshield 21," a mysterious running back wearing the number 21 jersey. the legendary eyeshield 21 can supposedly run at the speed of light and has achieved remarkable feats in the united states during his time at the notre dame college.\n\naccustomed to avoiding his problems in the past, sena's specialty might just help him become the new secret weapon of the deimon devil bats. as he interacts with his teammates, sena gradually gains more self-confidence and forges valuable bonds along the way.tv145finished airingtv tokyo music, nihon ad systems, shueisha, tv tokyoviz media, sentai filmworksgallopmanga23 min per epPG-13 - Teens 13 or older6881252199786524177688https://cdn.myanimelist.net/images/anime/1079/133529.jpg0000000001000000000000
616hachimitsu to clover8.00['comedy', 'romance', 'drama']yuuta takemoto, a sophomore at an arts college, shares a cheap apartment with two seniors—the eccentric shinobu morita, who keeps failing to graduate due to his absenteeism, and the sensible takumi mayama, who acts as a proper senior to takemoto, often looking out for him.\n\ntakemoto had not given much thought to his future until one fine spring day, when he meets the endearing hagumi hanamoto and falls in love at first sight. incredibly gifted in the arts, hagumi enrolls in takemoto's university and soon befriends the popular pottery student ayumi yamada. ayumi is already well acquainted with the three flatmates and secretly harbors deep feelings for one of them.\n\nhachimitsu to clover is a heartwarming tale of youth, love, soul-searching, and self-discovery, intricately woven through the complex relationships between five dear friends.tv24finished airinggenco, dentsu, asmik ace, shueisha, fuji tvviz media, discotek mediaj.c.staffmanga23 min per epPG-13 - Teens 13 or older589862413681747260166https://cdn.myanimelist.net/images/anime/1301/133577.jpg0000010000100000010000
717hungry heart wild striker7.55['comedy', 'slice of life', 'sports']as the younger brother of japanese soccer star seisuke kanou, kyousuke was always expected to grow as a soccer player at the same pace his brother did—an expectation that proved too difficult to meet. having fallen behind, he now lives in the shadow of his brother's success.\n\nentering his freshman year at jouyou akanegaoka high school, kyousuke vows never to play soccer again. however, miki tsujiwaki, the captain of the girls' soccer team, and mori kazuto, the manager of the boys' team, recognize kyousuke's potential and want to see his return to the game for their own reasons.\n\nwith an opportunity to play soccer again, kyousuke must either remain steadfast in his decision to abandon the sport he once loved, or allow himself to reignite that flame to become the best striker in the world.tv52finished airing--nippon animationmanga23 min per epPG-13 - Teens 13 or older155142122371296024172https://cdn.myanimelist.net/images/anime/12/49655.jpg0100000001100000000000
818initial d fourth stage8.16['action', 'drama']takumi fujiwara finally joins ryousuke and keisuke takahashi to create "project d." their goal is twofold: ryousuke wants to develop his "high-speed street racing theory," while keisuke and takumi aim at improving their driving skills by facing powerful opponents on dangerous roads. the idea of project d is to challenge street racing teams from other prefectures to improve both their uphill and downhill records. in order to attract the attention of the best racing teams, ryousuke creates a dedicated website to announce the future battles of project d and post the team's results.\n\nthe fourth season of initial d details the hardships and successes of the members of project d as they try to become the best street racing team outside of gunma prefecture.tv24finished airingob planning, studio jackfunimationa.c.g.t.manga27 min per epPG-13 - Teens 13 or older3931273123797878173710https://cdn.myanimelist.net/images/anime/9/10521.jpg0000000000000000010001
919monster8.87['suspense', 'mystery', 'drama']dr. kenzou tenma, an elite neurosurgeon recently engaged to his hospital director's daughter, is well on his way to ascending the hospital hierarchy. that is until one night, a seemingly small event changes dr. tenma's life forever. while preparing to perform surgery on someone, he gets a call from the hospital director telling him to switch patients and instead perform life-saving brain surgery on a famous performer. his fellow doctors, fiancée, and the hospital director applaud his accomplishment; but because of the switch, a poor immigrant worker is dead, causing dr. tenma to have a crisis of conscience.\n\nso when a similar situation arises, dr. tenma stands his ground and chooses to perform surgery on the young boy johan liebert instead of the town's mayor. unfortunately, this choice leads to serious ramifications for dr. tenma—losing his social standing being one of them. however, with the mysterious death of the director and two other doctors, dr. tenma's position is restored. with no evidence to convict him, he is released and goes on to attain the position of hospital director. \n\nnine years later when dr. tenma saves the life of a criminal, his past comes back to haunt him—once again, he comes face to face with the monster he operated on. he must now embark on a quest of pursuit to make amends for the havoc spread by the one he saved.tv74finished airingnippon television network, shogakukan-shueisha productions, vapviz mediamadhousemanga24 min per epR+ - Mild Nudity26142472353685691013100https://cdn.myanimelist.net/images/anime/10/18793.jpg0000000000000100011000
idtitlescoregenressynopsistypeepisodesstatusproducerslicensorsstudiossourcedurationratingrankpopularityfavoritesscored_bymembersimage_urlgenre_award_winninggenre_slice_of_lifegenre_fantasygenre_sci_figenre_eroticagenre_romancegenre_horrorgenre_boys_lovegenre_girls_lovegenre_sportsgenre_comedygenre__genre_gourmetgenre_suspensegenre_supernaturalgenre_avant_gardegenre_hentaigenre_dramagenre_mysterygenre_adventuregenre_ecchigenre_action
2373855723the forgotten princess just wants peace-1.0['fantasy', 'romance']i'm the daughter of a duke?!\nwhile locked up in a prison cell, eluana vita awakens her alchemy powers and later finds out she's the daughter of a duke! however, she's in for a rude awakening when she realizes the royal life isn't what she thought it'd be. join eluana on her journey to find the peaceful life she's always wanted.\n\n(source: manta)ona-1finished airing---web novel4 minG - All Ages000-10https://cdn.myanimelist.net/images/anime/1887/136575.jpg0010010000000000000000
2373955724beauty and the brawn-1.0['boys love', 'comedy']haeun is a nobleman who falls hard for dullseh, his gorgeous, but extremely clueless manservant. dullseh's impressive build reminds haeun of the sexy men from gay erotica that he loves so much. exams and career ambitions be damned—he just can't get dullseh out of his mind! silly hijinks ensue as haeun will do whatever it takes to lure the hunky dullseh into his bedchamber and possibly... his heart?!\n\n(source: tapas media)ona6finished airing---manga5 min per ep-000-10https://cdn.myanimelist.net/images/anime/1269/136576.jpg0000000100100000000000
23740557254 week lovers-1.0['boys love']wanna fake-date for 4 weeks?\ndojun's life at college is off to a perfect start! until he meets his roommate, jaehee, a high school friend whom he may have ghosted. jaehee claims he's over their unresolved past, but after an accident resulting in a fractured wrist, he makes dojun a proposition...\n\n(source: manta)ona10finished airing---web manga5 min per ep-000-10https://cdn.myanimelist.net/images/anime/1443/136577.jpg0000000100000000000000
2374155726die, please-1.0['fantasy', 'romance']i just want to tell him how i feel!\nmina has been planning on telling yeomyung she likes him. but every time she goes to confess, something foils her plans. it turns out yeomyung and youngwoong, her best friend, were actually plotting to kill her. but why?!\n\n(source: manta)ona-1finished airing---web manga5 minG - All Ages000-10https://cdn.myanimelist.net/images/anime/1621/136578.jpg0010010000000000000000
2374255728wo mengjian ni mengjian wo-1.0['drama']music video for the song wo mengjian ni mengjian wo by yoga lin.music1finished airing---original4 minG - All Ages000-10https://cdn.myanimelist.net/images/anime/1171/136582.jpg0000000000000000010000
2374355729thailand-1.0['avant garde']music video for the song thailand by oh shu.music1finished airing---original4 minPG-13 - Teens 13 or older000-10https://cdn.myanimelist.net/images/anime/1887/136583.jpg0000000000000001000000
2374455730energy-1.0['avant garde']music video for the song energy by sleeq (kim ryeong-hwa).music1finished airing---original4 minPG-13 - Teens 13 or older000-10https://cdn.myanimelist.net/images/anime/1817/136584.jpg0000000000000001000000
2374555733di yi xulie-1.0['adventure', 'fantasy', 'action', 'sci-fi']-ona16finished airing---web novelUnknownPG-13 - Teens 13 or older000-10https://cdn.myanimelist.net/images/anime/1130/137242.jpg0011000000000000000101
2374655734bokura no saishuu sensou-1.0['-']a music video for the song bokura no saishuu sensou by shannon.music1finished airing---original3 minPG-13 - Teens 13 or older000-10https://cdn.myanimelist.net/images/anime/1931/137236.jpg0000000000010000000000
2374755735shijuuku nichi-1.0['-']a music video for the song shijuuku nichi by shannon.music1finished airing---original3 minPG-13 - Teens 13 or older000-10https://cdn.myanimelist.net/images/anime/1902/136595.jpg0000000000010000000000